# Deep Learning and Computer Vision for Glaucoma Detection: A Review

Mona Ashtari-Majlan, Mohammad Mahdi Dehshibi, David Masip M. A. and D. M. are
with the Department of Computer Science, Multimedia, and Telecommunications,
Universitat Oberta de Catalunya, Barcelona, Spain. M. M. D. is with the
Department of Computer Science and Engineering, Universitat Carlos III de
Madrid, Madrid, Spain. M.A. (e-mail<EMAIL_ADDRESS>mashtarimajlan@uoc.edu).Manuscript #TPAMI-XYZ received XYZ; revised XYZ.

###### Abstract

Glaucoma is the leading cause of irreversible blindness worldwide and poses
significant diagnostic challenges due to its reliance on subjective
evaluation. However, recent advances in computer vision and deep learning have
demonstrated the potential for automated assessment. In this paper, we survey
recent studies on AI-based glaucoma diagnosis using fundus, optical coherence
tomography, and visual field images, with a particular emphasis on deep
learning-based methods. We provide an updated taxonomy that organizes methods
into architectural paradigms and includes links to available source code to
enhance the reproducibility of the methods. Through rigorous benchmarking on
widely-used public datasets, we reveal performance gaps in generalizability,
uncertainty estimation, and multimodal integration. Additionally, our survey
curates key datasets while highlighting limitations such as scale, labeling
inconsistencies, and bias. We outline open research challenges and detail
promising directions for future studies. This survey is expected to be useful
for both AI researchers seeking to translate advances into practice and
ophthalmologists aiming to improve clinical workflows and diagnosis using the
latest AI outcomes.

###### Index Terms:

Glaucoma, Deep Learning, Computer Vision, Machine learning

##  1 Introduction

Glaucoma is the second leading cause of irreversible blindness worldwide,
affecting over 70 million people as of 2020 [1]. If left untreated, glaucoma
leads to permanent vision loss due to damage to the optic nerve head and
retinal nerve fiber layer [2]. However, despite improved understanding and
management of glaucoma, it still accounts for approximately 10% of global
blindness [3, 4]. This high disease burden motivates the development of
enhanced diagnostic techniques to enable early diagnosis and timely
intervention to prevent or slow down the further deterioration of vision.

Accurately diagnosing glaucoma remains challenging for several reasons [5].
Firstly, glaucoma is often asymptomatic in its early stages, impeding
detection without comprehensive eye examinations. Secondly, current diagnostic
modalities like imaging tests and functional assessments have limitations in
sensitivity and specificity. For example, while optical coherence tomography
effectively captures structural changes in retinal layers, it cannot detect
early functional loss. In contrast, visual field testing assesses functional
impact but has low sensitivity for structural changes. Finally, the wide
variability in glaucoma presentation, from subtle early symptoms to severe
late-stage damage, makes definitive diagnoses difficult [2]. The complexity
and subjectivity of evaluating diverse examination findings further complicate
diagnosis.

This survey aims to provide a comprehensive overview of applying deep learning
and computer vision algorithms to enhance glaucoma diagnosis111When we use the
term “diagnosis” in the context of papers that focus on deep learning and
computer vision, we are referring to the use of AI technology to support
medical diagnosis. by overcoming these challenges. Such automated techniques
offer the potential for earlier detection, more consistent quantification of
progression, and ultimately preserving vision that would otherwise be lost to
glaucoma [6, 7]. Synthesizing recent techniques, results, and open problems
can deliver value to both ophthalmology practitioners and AI researchers. For
ophthalmology practitioners, it highlights cutting-edge research to improve
diagnostics accuracy and integrate intelligent systems into clinical
workflows. For AI researchers, it provides a landscape analysis of the state-
of-the-art, remaining gaps, and future opportunities to advance glaucoma
diagnosis algorithms.

The remainder of the paper is organized as follows: Section 2 outlines the
employed search protocol to select relevant papers for review, ensuring
comprehensive topic coverage. Section 3 discusses clinical terminologies and
definitions used in ophthalmology, enabling AI researchers to grasp the
essential concepts necessary for interpreting the paper. Section 4 delves into
the datasets used for training and testing deep learning and computer vision
models and the performance metrics employed to evaluate their effectiveness.
Section 5 explores the various types of features, including structural,
statistical, and hybrid, extracted for glaucoma diagnosis. Section 6 reviews
the latest research on developing end-to-end deep learning models for glaucoma
diagnosis, categorizing them based on the type and architecture of the models.
This section highlights the ability of deep learning models to integrate
multiple modalities, analyze complex data, and provide accurate diagnosis and
monitoring. Section 7 focuses on the methods proposed for early glaucoma
prediction, shedding light on the advancements in prognostic techniques. In
Section 8, challenges and potential future directions are discussed,
addressing the limitations and paving the way for further research in the
field. Finally, Section 9 serves as the conclusion, summarizing the key
findings and contributions. It emphasizes the transformative potential of deep
learning and computer vision in revolutionizing glaucoma diagnosis and
management.

##  2 Search protocol

A systematic search was conducted to identify relevant studies on deep
learning and computer vision techniques for glaucoma diagnosis published
between 2017 and 2023. This date range was selected to capture the state-of-
the-art advancements in this rapidly progressing field.

The following scholarly databases were searched: Web of Science, PubMed, IEEE
Xplore, and Google Scholar. Targeted search terms included “glaucoma,” “deep
learning,” “computer vision,” “machine learning,” “artificial intelligence,”
and “ medical diagnosis” keywords. These were combined using Boolean operators
and customized search strings tailored to each database. When available,
searches were restricted to titles, abstracts and author keywords to filter
potentially relevant papers efficiently. We also gave priority to leading
conferences and journals in deep learning, computer vision, medical imaging,
and ophthalmology.

After deduplication, the records retrieved underwent two-phase screening.
Title/abstract screening evaluated relevance to glaucoma diagnosis using deep
learning and computer vision approaches. The full-text review then confirmed
papers met the inclusion criteria: (1) written in English; (2) primary focus
on automated glaucoma diagnosis/screening using deep learning or computer
vision; (3) rigorous machine learning experiments and substantial technical
depth. Studies were excluded if they: (1) focused on other ocular diseases,
even with glaucoma sub-analysis; (2) primarily contributed clinically focused
insights. We also excluded review papers, case reports, and conference
abstracts given limited methodological detail.

Additionally, the reference lists of included papers were manually searched to
identify any additional relevant studies that might have been missed in the
initial search. This way, we could include seminal papers even if published
outside the target venues. Data extraction from the selected papers involved
capturing key information such as study design, dataset characteristics, deep
learning architectures, evaluation metrics, and major findings. This
information serves as the foundation for synthesizing the current state of
research in glaucoma diagnosis using deep learning and computer vision.

While this search protocol aimed to be comprehensive, some relevant studies
may have been inadvertently omitted, given the rapid pace of research in this
field. Nonetheless, the systematic selection aimed to identify a
representative sample for assessing the state-of-the-art deep learning and
computer vision techniques for glaucoma diagnosis.

##  3 Glaucoma: Definition and Diagnosis

Familiarity with key ophthalmic concepts and terminologies [8] is necessary,
specifically for AI researchers, to effectively interpret glaucoma diagnosis
research. This section defines relevant terms and tests used in clinical
glaucoma assessment, with a summary provided in Table I.

TABLE I: Ophthalmic terminologies.

Terminology |  Abbreviation |  Definition
---|---|---
Retinal Ganglion Cells |  RGCs |  Neurons in the innermost layer of the retina which receive and transmit visual information to the brain.
Retinal Nerve Fiber Layer |  RNFL |  Layer of RGCs’ nerve fibers (i.e., axons) that comprises the optic nerve and extends into the retina.
Ganglion Cell Layer |  GCL |  Layer of RGCs’ cell bodies.
Inner Plexiform Layer |  IPL |  Layer of RGCs’ dendrites.
Ganglion Cell Complex |  GCC |  Forms the combination of RNFL, GCL, and IPL layers.
Ganglion Cell with the Inner Plexiform Layer |  GCIPL |  Layer that consists of GCL and IPL.
Optic Nerve Head |  ONH |  The structure in the posterior section of the eye that enables the exit of axons of RGCs and the entry/exit of blood vessels.
Neuroretinal Rim |  NRR |  Area of the optic nerve head composed of retinal ganglion cell axons.
Optic Disc |  OD |  The optic disc and optic nerve head are interchangeable terms.
Optic Cup |  OC |  Central depression in the optic nerve head.
Cup-to-Disc Ratio |  CDR |  Quantitative measure comparing the size of the optic cup to the optic disc.
Intraocular Pressure |  IOP |  Fluid pressure inside the eye, influenced by the balance of aqueous humor production and drainage.

Glaucoma is a condition characterized by the degeneration of Retinal Ganglion
Cells (RGCs), leading to structural changes in the retina [9]. These changes
manifest as (1) the thinning of the Retinal Nerve Fiber Layer (RNFL), Ganglion
Cell with the Inner Plexiform Layer (GCIPL), and Ganglion Cell Complex (GCC)
profiles, (2) narrowing Neuroretinal Rim (NRR), and (3) cupping of the Optic
Nerve Head (ONH) or enlargement of the Cup-to-Disc Ratio (CDR) [2] (see Fig.
1). In addition to these structural changes, glaucoma also causes functional
damage, resulting in defects in visual field sensitivity [10].

Figure 1: Anatomical structures of the human eye and optic nerve relevant to
glaucoma detection. [Left] Schematic views, [Right] Fundus and OCT views [11].
This figure was created using images licensed under Creative Commons.

The process of detecting glaucoma is both complex and time-consuming [12]. To
gain valuable insights into the structural and functional changes associated
with the disease, medical examinations and clinical expertise are utilized,
where imaging techniques play a vital role. The National Institute for Health
and Care Excellence in the UK recommends the use of fundus imaging to examine
the ONH [13].

Fundus imaging is a method that takes detailed images of the retina and OD.
This helps with evaluating the appearance of the optic nerve, detecting
vascular changes, and identifying any abnormalities. This imaging modality is
a useful diagnostic tool for a variety of ocular conditions, including
glaucoma. Optical Coherence Tomography (OCT) is another non-invasive imaging
technique that produces highly detailed cross-sectional images of the retina,
optic nerve, and other eye structures. It enables clinicians to visualize the
retinal layers, measure the thickness of the RNFL, and assess the integrity of
the NRR.

While fundus and OCT imaging techniques are commonly used to capture
structural changes associated with glaucoma, the Humphrey Visual Field
analysis (VF) measures the sensitivity of the visual field. This test, based
on standard automated perimetry, allows patients to respond to visual stimuli
presented at different locations within their visual field. By mapping the
patient’s responses, clinicians can identify visual field defects associated
with glaucoma and assess the extent of retinal sensitivity loss to provide
better ground truth [14, 15, 16].

The integration of fundus, OCT, and VF modalities [17, 18], in addition to
expert-level features [19] and biomarkers such as Intraocular Pressure (IOP)
[20, 21, 22], can leverage a broader range of inputs to improve the accuracy
and enhance the performance of deep learning models, enabling more precise
detection, prediction, and management of glaucoma.

##  4 Datasets and Evaluation Metrics

In this section, we present a concise overview of the datasets used in the
studies reviewed within this paper, as well as the evaluation metrics employed
to assess the performance of trained models in glaucoma diagnosis. To
facilitate ease of reference and enhance clarity, we have summarized this
information in a table format (refer to Table II222The links to the databases
were active at the time of submitting this paper. In case of any inactive
links, please contact the authors of the respective papers for updated
information.).

Various evaluation metrics have been used to assess the performance of
glaucoma diagnostic models. To account for the lack of standardization in
metrics across datasets, researchers have used both subject-level and finer-
grained spatial metrics in their studies. Equation 1 illustrates the commonly
used quantitative measures in the reviewed studies. These include accuracy
(ACC), sensitivity (SEN), specificity (SPE), precision (PRC), and the
F1-score. Here, $TP$, $TN$, $FP$, and $FN$ represent true positive, true
negative, false positive, and false negative, respectively. Additionally, the
area under the receiver operating characteristic curve (AUC) metric has been
employed to evaluate the performance of glaucoma diagnosis.

$\begin{split}\mathrm{Accuracy~{}(ACC)}=&~{}\frac{TP+TN}{TP+TN+FP+FN}.\\\
\mathrm{Sensitivity~{}(SEN)}=&~{}\frac{TP}{TP+FN},\\\
\mathrm{Specificity~{}(SPE)}=&~{}\frac{TN}{TN+FP},\\\
\mathrm{Precision~{}(PRC)}=&~{}\frac{TP}{TP+FP},\\\
\mathrm{F1-score}=&~{}\frac{2\times\mathrm{SEN}\times\mathrm{SPE}}{\mathrm{SEN}+\mathrm{SPE}}.\end{split}$
(1)

TABLE II: A review of the most commonly used datasets for glaucoma diagnosis.
GT: Ground Truth, G: Glaucoma, H: Healthy.

Dataset | Number of Images |  Resolution |  Ground Truth Description |  Note |  Address
---|---|---|---|---|---
Glaucoma |  Healthy |  Total
Fundus
REFUGE [23] |  121 |  1,079 |  1,200 |  2,124$\times$2,056, 1,634$\times$1,634 |  Subject-level label, Segmentation GT (OC/OD) |  - |  Link
LAG [24] |  4,878 |  6,882 |  11,760 |  500$\times$500 |  Subject-level label, Attention GT map |  5,824 out of all images (2,392 G/3,432 H) have Attention GT maps |  Link
RIM-ONE DL [25] |  172 |  313 |  485 |  Various |  Subject-level label |  Combination of RIM-ONE-r1, r2 and r3 [26], Images are cropped at ON |  Link
ORIGA [27] |  168 |  482 |  650 |  Various |  Subject-level label, Segmentation GT (OC/OD) |  - |  Available upon request
DRISHTI-GS1 [28] |  70 |  31 |  101 |  2,896$\times$1,944 |  Subject-level label, Segmentation GT (OC/OD), CDR Values, Notching |  DRISHTI-GS1 is an extension of DRISHTI-GS [29] |  Link
ACRIMA [30] |  396 |  309 |  705 |  2,048$\times$1,536 |  Subject-level label |  Images are cropped at ON |  Link
SIGF [31] |  - |  - |  3,671 |  Various |  Subject-level label |  405 Sequential fundus images for glaucoma forecast including an average of 9 images per eye |  Link
HRF [32] |  15 |  15 |  - |  3,504$\times$2,336 |  Subject-level label, Vessel GT |  It also contains 15 images from diabetic retinopathy patients |  Link
DRIONS-DB [33] |  - |  - |  110 |  600$\times$400 |  Contour of ON |  Of all the images, 23.1% belong to glaucoma patients and 76.9% to eye hypertension patients |  Link
ODIR-5K [34] |  307 |  1,620 |  - |  Various |  Subject-level label |  Contains 5,000 images divided into eight categories |  Link
JSIEC [35] |  13 |  54 |  |  Various |  Subject-level label |  Contains 1087 images divided into 37 categories |  Link
RIGA [36] |  - |  - |  750 |  Various |  Segmentation GT (OC/OD) |  No subject-level label |  Link
OCT
AGE [37] |  - |  - |  300 |  Various |  Subject-level label (Angle-closure Vs. Open-angle glaucoma)‡, Scleral Spur localization |  Each OCT volume contains 16 2D images resulting in 4,800 images (1:4 ratio of closure to open-angle samples) |  Link
OCT & Fundus
AFIO [11] |  32 |  18 |  50 |  OCT: 951$\times$456, Fundus: 2,032$\times$1,934 |  Subject-level label, CDR values |  The data is from 26 subjects |  Link
VF
Rotterdam [38] |  2,279 |  244 |  2,523 |  - |  Subject-level label |  24-2 VF test pattern |  Link
Budapest [38] |  532 |  1,735 |  2,267 |  - |  Subject-level label |  24-2 VF test pattern |  Link
‡ Glaucoma can be classified into two broad categories, angle-closure and
open-angle. The former is considered a more aggressive form of the disease
compared to the latter [37].

##  5 Feature Extraction

The feature extraction process involves deriving a set of representative
features from the input data. These features can be extracted using various
methods, such as conventional statistical or structural techniques (also known
as hand-crafted features), deep learning architectures that automatically
learn relevant features from the data, or biomarkers identified by domain
experts based on their knowledge. The resulting informative features are then
used as inputs to train the learning model.

###  5.1 Structural Features

Structural image measurements based on physical characteristics of the optic
nerve head help clinicians in quantifying retinal structures relevant to
glaucoma. These measurements provide objective information that can be used to
determine the severity of glaucoma and monitor its progression. Clinical
knowledge can help improve the accuracy and interpretability of machine
learning algorithms[39]. One of the most important structural measurements
that can be extracted from fundus images and is used by many clinicians is the
CDR. A higher CDR indicates a larger Cup and a higher risk of glaucoma [40].
Therefore, many glaucoma screening works focus on accurate OC and OD
segmentation tasks [41, 42, 43, 44, 45, 46, 47, 48, 49].

Soorya et al. [50], for instance, proposed an adaptive threshold framework for
segmenting the OC and OD based on geometrical features. Following a clinical
approach used by ophthalmologists, the proposed algorithm tracks blood vessels
inside the Disc region, identifies the points at which different vessels are
bent for the first time, and connects them to obtain the contours of the OC.
They further calculated the vertical CDR (the distance between the topmost and
bottommost points of the OC and OD) and proposed a threshold based on which
they classified an image as normal/suspect glaucoma/glaucoma. Mvoulana et al.
[51] proposed using the K-means clustering algorithm with an intensity-based
nearness criterion for pixel classification, followed by a model-based
boundary fitting approach employing the circular Hough transform for OC and OD
segmentation. They later calculated the CDR value and used a threshold to
classify healthy and glaucoma patients. The method achieved 98% accuracy on
the DRISHTI-GS1 dataset for the final glaucoma diagnosis.

Deep learning models have shown promising results in various image analysis
tasks, including image segmentation [52]. For example, Jiang et al. [53]
proposed to use two Faster R-CNNs [54] to segment the OC and OD separately and
find the minimal bounding boxes for the two regions. The authors extended
their work in [40], where they focused on the joint OC and OD segmentation
problem by proposing JointRCNN, an end-to-end region-based convolutional
neural network. The JointRCNN consists of four major parts: feature extraction
module, Disc proposal network, Disc attention module and Cup proposal network.
The feature extraction module is shared by OC and OD segmentation tasks, and
atrous convolution [55] is used to improve the feature extraction performance
in this module. The Disc and Cup proposal networks generate bounding box
proposals, and the Disc attention module is proposed to connect the two
networks. To improve the segmentation performance, Fu et al. [42] proposed
using polar transformation along with a multi-label deep network (M-Net) for
joint segmentation of the OC and OD in retinal images. The proposed M-Net
consists of a multi-scale input layer to construct an image pyramid, a U-shape
convolutional network to learn the rich hierarchical representation, a side-
output layer as an early classifier that produces a companion local prediction
map for different scale layers, and a multi-label loss function to generate
the final segmentation map. They calculated the CDR value for glaucoma
screening and evaluated the performance of the proposed method on the ORIGA
and Singapore Chinese Eye Study (SCES) datasets achieving an AUC of 85.08% and
89.98%, respectively. Liu et al. [45] proposed a joint OC and OD segmentation
method based on a semi-supervised model to take advantage of both labeled and
unlabeled data to improve the segmentation performance. The proposed
conditional Generative Adversarial Nets (cGAN)-based architecture consists of
a segmentation net, a generator and a discriminator to learn a mapping between
the fundus images and the corresponding segmentation maps. Both the
segmentation net and the generator in the proposed framework focus on learning
the conditional distributions between fundus images and their corresponding
segmentation maps. At the same time, the discriminator determines whether the
image-label pairs come from the empirical joint distribution. Furthermore, the
proposed method performed better than its fully-supervised version on both
ORIGA and REFUGE datasets, with AUC values of 86.22% and 90.11% and accuracy
rates of 76.57% and 82.78%, respectively.

While segmentation-based methods have shown effective performance, accurate
CDR measurement remains challenging due to factors such as overlapping
regions, low contrast between regions, shape variability and inhomogeneity of
the OD, insufficient labels, and errors in intermediate steps. Zhao et al.
[56] proposed a direct CDR estimation method based on a semi-supervised
learning scheme that bypasses the intermediate segmentation step. The proposed
two-stage cascaded approach consisted of two phases: unsupervised feature
representation of fundus image with a Convolutional Neural Network (CNN) and
CDR value regression using a Random Forest (RF) regressor. The proposed model
achieved an AUC of 90.50% for glaucoma diagnosis on a dataset of 421 fundus
images. Zhou et al. [57] proposed an adaptive weighted locality-constrained
sparse coding approach for glaucoma diagnosis, which combines locality
constraint with sparse constraint and employs a weighted locality constraint
constructed by adaptively combining multiple distance measurements. The
proposed approach achieved an accuracy of 88.63% and 85.56% in diagnosing CDR
for DRISHTI-GS and RIM-ONE-r2, respectively.

To predict the average RNFL thickness previously extracted from Spectral
Domain OCT (SD-OCT333SD-OCT is a type of OCT that uses a faster scanning speed
and higher resolution to produce more detailed images of the eye’s internal
structures compared to traditional time-domain OCT.) scans and allow for
quantification of neural damage, Medeiros et al. [58] proposed a deep learning
model based on fundus OD images. They used a private dataset containing 32,820
pairs of OD fundus images and SD-OCT scans to predict average RNFL thickness.
They also assessed the ability of predicted and actual RNFL thickness values
to discriminate between glaucomatous and healthy eyes and achieved an AUC of
94.00% and 94.40%, respectively. Raja et al. [59] proposed a novel approach to
objectively grade glaucoma as an early suspect or advanced stage based on the
degeneration of RGCs. They first segmented the RNFL, GCIPL, and GCC regions
and extracted their thickness information. Subsequently, they employed a
Support Vector Machine (SVM) to evaluate the severity of glaucoma, achieving a
high accuracy of 91.17% on the AFIO dataset. Lee et al. [60] also proposed a
deep learning framework based on NASNet [61] for diagnosing glaucoma using SD-
OCT images. They extracted features from the GCIPL thickness map, GCIPL
deviation map, RNFL thickness map, and RNFL deviation map and fed the
extracted features into the deep learning classifier for glaucoma diagnosis.
The proposed model achieved an AUC of 99.00% with a sensitivity of 94.7% and a
specificity of 100.0% on a private dataset with 350 glaucomatous and 307
healthy SD-OCT image sets.

###  5.2 Statistical Features

Statistical features such as intensity-based, texture-based, morphological,
and wavelet-based features are used to extract quantitative measurements from
the medical data [62]. In glaucoma diagnosis, these features can help
differentiate between normal and abnormal eyes. Claro et al. [63] conducted an
extensive study to determine the best set of features for fundus image
representation, which included Local Binary Pattern, Gray Level Co-occurrence
Matrix (GLCM) [64], Histogram of Oriented Gradients (HOG) [65], Tamura [66],
Gray Level Run Length Matrix (GLRM) [67], morphology, and seven CNN
architectures, yielding a 30,682-D feature vector. They then used the gain
ratio algorithm for feature selection and concluded that a combination of the
GLCM and pre-trained CNNs has the best glaucoma diagnosis accuracy of 92.78%
on 1675 images of DRISHTI-GS, RIM-ONE, HRF, JSIEC, and ACRIMA datasets.

Juneja et al. [68] extracted GLRM and GLCM features from the wavelet-filtered
OCT images, along with 18 other statistical features. Thereafter,
discriminative features were selected using the gain ratio, information gain
and correlation statistical methods. In addition, they used a 3D-CNN
architecture to extract features and perform classification. They finally used
majority voting and weighted decision fusion strategies to provide the final
classification results taking into account K-nearest neighbour (k-NN), RF, SVM
and the probability given by the 3D-CNN model. Based on the experimental
results, the proposed framework achieved a precision of 95.00%, sensitivity of
97.00% and F1-score of 96% on a private dataset of 1,110 OCT scans (847
glaucoma cases and 263 normal cases). Maheshwari et al. [69] proposed using
empirical wavelet transform (EWT) to decompose fundus images and obtaining
correntropy features from decomposed EWT components for glaucoma diagnosis.
These extracted features are then ranked based on the t-value feature
selection algorithm and fed to a least-squares SVM for normal and glaucoma
image classification, achieving an accuracy of 98.33% and a specificity of
96.67% on a private and a public database. Nayak et al. [70] proposed an
automatic feature extraction method based on a meta-heuristic optimization
algorithm called a real-coded genetic algorithm. To extract high-level
features from the fundus images directly, the proposed method adopts a
strategy based on maximizing the inter-class distance and minimizing intra-
class variability. The final feature vectors are then used in conjunction with
an SVM classifier for glaucoma diagnosis. The experimental results on a
dataset of 1,426 fundus images (589 normal and 837 glaucoma) yielded an
accuracy of 97.20%.

Extracting the Region of Interest (ROI) speeds up subsequent processing by
excluding irrelevant image regions. As previously stated, the OD is an
important ROI region in glaucoma diagnosis. For example, Vinícius dos Santos
Ferreira et al. [71] proposed a framework for OD semantic segmentation based
on the U-Net model. Texture features were then extracted from both the RGB
channels and gray levels of the segmented region using phylogenetic diversity
indexes. The proposed approach resulted in an accuracy of 98.50%, with a
sensitivity of 98.00%, specificity of 100%, F1-score of 96.00%, and AUC of
98.10% on the RIM-ONE, DRIONS-DB, and DRISHTI-GS datasets for glaucoma
diagnosis. Bisneto et al. [72] also proposed a cGAN with a U-Net generator and
a PatchGAN discriminator for OD segmentation. They used cGAN in conjunction
with taxonomic indexes to extract textural attributes from the segmented OD
region for glaucoma classification. Three different classifiers, namely
Multilayer Perceptron (MLP), Sequential Minimal Optimization, and RF, were
utilized in diagnosing glaucoma on the RIM-ONE and DRISHTI-GS datasets. All of
the classifiers achieved 100% accuracy and AUC.

###  5.3 Hybrid Features

Combining structural and statistical features can improve the performance of
glaucoma diagnosis models by incorporating different perspectives. While
structural features provide information about clinical measurements and
anatomical changes in the retina, statistical features provide image-based
information. As a result, more comprehensive information for effective
glaucoma screening can be obtained by utilizing hybrid features. To this end,
Balasubramanian and N.P. [73] extracted structural and statistical features
from fundus images and presented correlation-based feature selection
algorithms as well as a Kernel-Extreme Learning Machine classifier for
glaucoma diagnosis. To extract structural features, they first segmented OD
and OC using a Fuzzy C-Means Clustering algorithm and calculated CDR and Cup
shape features. They further extracted features like GLCM, Anisotropic Dual-
Tree Complex Wavelet Transform (ADT-CWT) [74], Fractal Texture Analysis [75],
SURF [76], Pyramid HOG [77], Mean Gray-Level, Color Intensity Features, and
Super-pixels. The experimental results demonstrated a maximum overall accuracy
of 99.61% with 99.89% sensitivity and 100% specificity on the public and
private retinal fundus datasets containing 7,280 images. Guo et al. [78]
proposed the increasing field of view (IFOV) feature model to fully extract
textural, statistical, and other hidden image-based features. In the IFOV
model, there are four different image scales, ranging from small to large: OC
region, OD region, ROI (cropped image around OD), and global fundus image.
They then extracted CDR and statistical features from images in different
scales using the Gabor transform and GLCM, followed by feature selection using
the adaptive synthetic sampling approach [79]. Finally, the extracted features
are used to train a gradient-boosting decision tree classifier for glaucoma
screening resulting in 84.30% and 83.70% accuracy on ORIGA and DRISHTI-GS1
datasets, respectively.

Thakur and Juneja [80] presented a set of reduced hybrid features derived from
structural and nonstructural features to classify the retinal fundus images.
The structural features extracted included CDR and Disc damage likelihood
scale. Whereas, non-structural features included GLRM, GLCM, First order
statistical, Higher order spectra, Higher order cumulant and Wavelets. They
performed feature selection using the wrapper approach and used different
classifiers, including k-NN, Neural network (NN), RF, SVM, and Naïve Bayes
(NB), for glaucoma diagnosis. Among all the classifiers, SVM exhibited the
highest performance with an accuracy of 97.20%, a specificity of 96.00%, a
precision of 97.00%, and a sensitivity of 97.00% on the DRISHTI-GS and RIM-ONE
datasets. Kausu et al. [81] proposed a novel method for glaucoma
identification based on time-invariant feature CDR and ADT-CWT features. They
first segmented the OD using the Fuzzy C-Means clustering method, and the OC
using Otsu’s thresholding. An MLP model is finally used for glaucoma
classification achieving an accuracy of 97.67% with 98% sensitivity. The
dataset used in this paper was collected from the Venu Eye Institute &
Research Centre in New Delhi, India, and contained a total of 86 images, 51 of
which were healthy and 35 from glaucoma patients.

The Inferior Superior Nasal Temporal (ISNT) rule is one of the widely used
techniques for assessing structural damage to the optic nerve head in clinical
practice. According to the ISNT rule, the thickness of the NRR in normal eyes
decreases in the following order: inferior region $>$ superior region $>$
nasal region $>$ temporal region, whereas the NRR in glaucomatous optic discs
violates this rule [82]. Pathan et al. [83] extracted clinical features from
the segmented OD and OC regions, including CDR estimation and ISNT rule
verification in the NRR area. They further extracted color and texture
features from the NRR area to analyze glaucoma-related changes in fundus
images. Three color spaces were used to extract color features, including RGB,
CIEL*a*b, and HSV, while textural features were extracted using the GLCM
approach. Finally, to diagnose glaucoma from normal samples, they used an SVM,
a three-layered NN, and an AdaBoost classifier with dynamic ensemble
selection. The SVM algorithm demonstrated the highest accuracy of 95.00% and
90.00% on the DRISHTI-GS1 and a private dataset, respectively. Singh et al.
[84] also extracted ISNT regions and CDR, along with 20 statistical features
such as Homogeneity, Contrast, and Correlation. They used the combination of
four machine learning algorithms (i.e., SVM, K-NN, NB, and a 3-layered MLP)
and achieved 95.82% sensitivity, 98.59% specificity with an accuracy of 98.60%
on the DRIONS-DB dataset. Martins et al. [85] segmented the OC and OD and
calculated morphological features. The extracted features included CDR,
vertical length CDR, Rim-to-disc area ratio, which also provides an
interpretation of the ONH shape, and ISNT values and rule compliance. The
proposed pipeline also included a glaucoma confidence level assessment using a
classification network with a MobileNetV2 [86] feature extractor as a
backbone. The final decision combines the glaucoma confidence level with the
calculated morphological features, yielding an accuracy of 87.00%, sensitivity
of 85.00%, specificity of 88.00%, and AUC of 93.00% on a merged dataset of
several publicly available datasets.

Different types of ophthalmic images provide information on retinal pathology
from various angles, and combining them can aid in glaucoma diagnosis. Chen et
al. [17] proposed an automatic method for early glaucoma screening using
Enhanced Depth Imaging OCT (EDI-OCT) and fundus images. The method includes
segmenting the anterior lamina cribrosa surface in EDI-OCT images with a
region-aware strategy and residual U-Net and extracting structural features
such as lamina cribrosa depth and deformation. Similarly, in fundus images,
scanning lines and brightness compensation are used to segment the OC and OD
regions, and the CDR and textural features are extracted. Hybrid features that
combine structural parameters from EDI-OCT and textural features from fundus
images are then used for training and classification to screen glaucoma in the
early stage using gcForest. The proposed method achieved an accuracy of 96.88%
with 91.67% sensitivity on a private dataset.

##  6 End-to-end Glaucoma Classification

End-to-end deep learning models have shown promising results in glaucoma
classification, outperforming feature-based methods [87, 88, 89, 90, 91, 92,
93, 22, 39]. Because of their ability in incorporating holistic contextual
information in the training process, end-to-end deep learning models can
potentially reduce the risk of information loss and improve generalizability.
For example, Hemelings et al. [94] showed that end-to-end deep learning models
take advantage of contextual information outside the optic nerve head region
in fundus images to detect glaucoma and estimate the CDR. In this section, we
review studies that mainly focus on these models for diagnosing glaucoma
disease. We classify these models mainly based on their architectures into
Convolutional Neural Networks, Autoencoder-based Networks, Attention-based
Networks, Generative Adversarial Networks, Geometric Deep Learning Networks,
and Hybrid Networks.

###  6.1 Convolutional Neural Networks

Convolutional neural networks (CNNs) have been widely used for glaucoma
diagnosis [95, 96, 97, 98, 99, 38]. In general, these models are used to
extract higher-level features from raw image data, with earlier layers
focusing on simple features such as colors and edges and later layers
identifying more complex shapes and structures. The CNN architecture consists
of different types of layers, including convolutional, pooling, and fully
connected layers. Convolutional layers apply learned filters to the input
image, generating activation feature maps that represent detected features of
different complexity and level of detail. The pooling layer is used to reduce
the dimensionality of the feature maps to decrease computational complexity.
Fully connected layers finally map the extracted high-level features to the
desired output classes. Many studies reviewed in this paper employed state-of-
the-art CNN architectures, including Inception-v3 [100], ResNet [101],
EfficientNet [102], and DenseNet [103] pre-trained on ImageNet [104]. These
studies either used the pre-trained architectures without further
modifications or slightly modified them to fit the research’s objectives.
Additionally, some studies developed new CNN architectures entirely from
scratch, tailoring the model design specifically for the glaucoma
classification task. Table III summarize the papers that used CNN for glaucoma
classification.

TABLE III: Comparison of papers utilizing CNN architectures for glaucoma
classification. Studies with (*) mark tested their model on an auxiliary
dataset. N: Normal, G: Glaucoma, S: Glaucoma Suspect, A: Advanced glaucoma, E:
Early glaucoma, Avg: Average.

Study |  Model |  Data type |  Dataset | Classification Performance Measures (%) |  Code
---|---|---|---|---|---
ACC |  SEN |  SPE |  PRC |  F1-score |  AUC
[88] |  18-layer CNN |  Fundus |  Private (589 N, 837 G) |  98.13 |  98.00 |  98.30 |  - |  - |  - |  -
[105]* |  EfficientNet-based |  Fundus |  Private (1,586 G, 2,244 N), RIGA |  93.29 |  96.03 |  91.42 |  - |  - |  98.29 |  Link
LAG |  95.81 |  98.40 |  94.22 |  - |  - |  99.49
[96] |  Inception-v3 |  Fundus |  Private (29,466 N, 2,620 S, 7,659 G) |  - |  95.60 |  92.00 |  - |  - |  98.60 |  -
[97] |  TRk-CNN |  Fundus |  Private (403 N, 208 S, 381 G) |  88.94 (Avg) |  85.37 (S Vs. N), 90.36 (G Vs. N) |  89.33 (Avg) |  74.47 (S Vs. N), 94.94 (G Vs. N) |  79.55 (S Vs. N), 92.59 (G Vs. N) |  - |  -
[106] |  ResNet-18 |  Fundus |  Private (Training: 1,424 G, 1,818 N) |  - |  - |  - |  - |  - |  96.50 |  -
[107] |  3-layer CNN |  Fundus |  Private (1,542, 786 N, 467 A, 289 E) |  87.90 |  - |  - |  - |  - |  94.00 |  -
[98] |  WSMTL |  Fundus |  ORIGA |  - |  - |  - |  - |  - |  92.00 |  -
[108] |  EAMNet |  Fundus |  ORIGA |  - |  - |  - |  - |  - |  88.00 |  -
[21] |  ResNet |  Fundus, VF |  Private (1,695 N, 1,201 mild, 1,607 moderate, 1,868 severe) |  85.50 (G Vs. N), 82.50 (Multi-class‡) |  - |  - |  - |  - |  88.10 (G Vs. N), 95.60 (Multi-class) |  -
[38]* |  7-layer CNN |  VF |  Rotterdam |  - |  - |  - |  87.40, 98.60 |  - |  - |  Link
Budapest |  - |  - |  - |  98.60 |  - |  -
[109]* |  CNN |  OCT |  HK (2,926 G, 1,951 N) |  92.70 |  - |  - |  - |  94.10 |  97.70 |  -
Stanford (806 G, 425 N) |  86.00 |  - |  - |  - |  88.90 |  93.30
[99] |  ResNet-34 |  OCT |  Private (2,926 G, 1,951 N) |  91.00 |  89.00 |  96.00 |  - |  - |  96.90 |  -
[87] |  ResNet-34 |  OCT |  Private (612 G, 542 N) |  - |  - |  - |  - |  - |  96.00 |  -
[110]* |  Inception-v3 |  OCT |  Private (192 G, 545 N) |  90.40 |  - |  - |  - |  - |  - |  Link
Private (57 G, 44 N) |  91.10 |  - |  - |  - |  - |  -
[111] |  6-layer CNN |  OCT |  Private (1,579 G, 359 N) |  - |  - |  - |  - |  - |  93.70 |  -
‡ Severity classification between mild, moderate and severe glaucoma

Wang et al. [109] proposed an end-to-end semi-supervised multi-task learning
CNN for classifying OCT B-scan images as glaucoma or normal and investigating
the relationship between structural and functional changes in glaucoma eyes.
The proposed CNN comprises three components: a shared feature extraction
module with a ResNet-18 backbone, a glaucoma classification module, and a VF
measurement regression module. To develop and test the proposed method, they
also created one of the largest glaucoma OCT image datasets (i.e., HK dataset)
with 975,400 B-scans from 4,877 volumes. Zhao et al. [98] also proposed a
Weakly-Supervised Multi-Task Learning (WSMTL) method for accurate evidence
identification, OD segmentation, and automated glaucoma diagnosis. WSMTL
consists of a skip and densely connected CNN for multi-scale feature
representation of fundus structure, a pyramid integration structure for
generating high-resolution evidence maps, a constrained clustering branch for
OD segmentation, and a fully-connected discriminator for automated glaucoma
diagnosis. The model is trained on weakly labeled data with binary diagnostic
labels (normal/glaucoma), and the output is a pixel-level segmentation mask
and glaucoma diagnosis label. Liao et al. [108] proposed a clinically
interpretable CNN architecture (EAMNet) for glaucoma diagnosis. EAMNet
aggregates the features extracted from a CNN backbone with ResBlock at various
scales to bridge the gap between semantic and localization information at
multiple scales in order to improve glaucoma diagnosis accuracy. Additionally,
EAMNet generates refined evidence activation maps that highlight the glaucoma-
specific discriminative regions recognized by the network, aiming to provide a
more transparent interpretation.

Xue et al. [21] proposed a three-phased framework for 1) screening, 2)
detecting glaucoma from normal, and 3) classifying glaucoma into four severity
levels. In the first phase, they used IOP to screen out patients with
glaucoma. Two distinct ResNet architectures, namely DetectionNet and
ClassificationNet, were trained independently during the second and third
phases, respectively. In the case of DetectionNet, the fusion of fundus and
Voronoi VF images served as input for the classification of normal and
glaucoma cases. Conversely, ClassificationNet utilized a Voronoi VF image as
input to categorize glaucoma severity into mild, moderate, or severe. Jun et
al. [97] also proposed a Transferable Ranking Convolutional Neural Network
(TRk-CNN) for the multi-class classification of normal, glaucoma suspect, and
glaucoma eyes. TRk-CNN employs DenseNet as the backbone CNN model and combines
the weights of the primitive classification model to reflect inter-class
information to the final classification phase, where there is a high
correlation between classes.

###  6.2 Autoencoder-based Networks

An autoencoder is a type of neural network that uses an encoder-decoder
structure to extract important features from input data. The encoder maps the
input into a lower-dimensional latent space, and the decoder maps the latent
representation back to the original input space. This mechanism allows the
autoencoder to capture meaningful data representations, which has significant
potential for improving glaucoma detection methodologies. For instance,
Raghavendra et al. [90] proposed a two-layer sparse autoencoder to extract
effective and important features from fundus images for glaucoma detection.
The proposed network consists of two cascaded autoencoders for unsupervised
feature learning and a Softmax layer for supervised glaucoma classification.
Table IV summarizes the papers that used an autoencoder-based architecture for
glaucoma diagnosis.

TABLE IV: Comparison of papers utilizing autoencoder-based architectures for
glaucoma classification. Studies with (*) mark tested their model on an
auxiliary dataset. SAE: Sparse Autoencoder, N: Normal, G: Glaucoma.

Study |  Model |  Data type |  Dataset | Classification Performance Measures (%) |  Code
---|---|---|---|---|---
ACC |  SEN |  SPE |  PRC |  F1-score |  AUC
[89]* |  U-Net-based |  Fundus |  REFUGE |  - |  - |  - |  - |  - |  97.60 |  -
DRISHTI-Gs |  - |  - |  - |  - |  - |  94.74 |  -
[112] |  U-Net-based |  Fundus |  REFUGE |  - |  - |  - |  - |  - |  96.76 |  -
[90] |  SAE |  Fundus |  Private (589 N, 837 G) |  95.30 |  95.20 |  - |  96.80 |  95.00 |  - |  -
[113] |  G-EyeNet |  Fundus |  DRIONS-DB |  - |  - |  - |  - |  - |  92.30 |  -
[59] |  RAG-Netv2 |  OCT |  AFIO |  94.91 |  97.14 |  91.66 |  94.44 |  95.77 |  98.71 |  Link

Pal et al. [113] proposed a deep learning model (G-EyeNet) for detecting
glaucoma using an encoder-decoder framework. G-EyeNet comprises an encoder,
decoder, and classifier module. The encoder-decoder structure is used for
image reconstruction and unsupervised feature learning, while the classifier
module uses the latent-space distribution learnt by the encoder to classify
glaucoma. The framework is trained using multi-task learning to minimize
reconstruction and classification losses. Raja et al. [59] proposed a hybrid
autoencoder-based convolutional network framework (RAG-Netv2) for segmenting
RGC regions and classifying glaucoma. RAG-Netv2 is first trained to extract
RGC regions, particularly the RNFL, GCIPL, and GCC regions. The RAG-Netv2
encoder end is then used to perform RGC-aware classification of healthy and
glaucoma samples.

Hervella et al. [89] proposed a multi-task approach for simultaneous OD and OC
segmentation and glaucoma classification. The proposed network consists of an
encoder-decoder structure that is shared between tasks and takes advantage of
both pixel-level and image-level labels during network training. In addition,
they used a multi-adaptive optimization strategy to ensure that both tasks
contribute equally to parameter updates during training, avoiding the use of
loss weighting hyperparameters. Pascal et al. [112] proposed a multi-task deep
learning model to detect glaucoma in retinal fundus images while segmenting
the OD and OC and locating the fovea. The proposed model is trained using a
U-Net encoder-decoder convolutional network as a backbone architecture and is
adapted to handle the four tasks using independent optimizers. The multi-task
model outperforms the single task of detecting glaucoma because it leverages
related tasks and their similarities to achieve better performance. Ren et al.
[114] also proposed a task decomposition framework based on an encoder-decoder
architecture for both semantic segmentation of OC and OD and glaucoma
classification. The three subsequent subtasks that they proposed are (1)
pixel-wise semantic segmentation of fundus images, (2) prediction of OD and OC
instance class labels, and (3) classification of glaucoma and normal fundus
images. The framework used a sync-regularization to penalize the deviation
between the outputs of pixel-wise semantic segmentation and the instance class
prediction tasks and outperformed the single-task model.

###  6.3 Attention-based Networks

The attention mechanism in deep learning enables models to selectively focus
on the most important parts of input data, enhancing prediction accuracy and
efficiency. Using attention in glaucoma screening models intuitively satisfies
the need to focus on the key pathological areas rather than other redundant
information. In the reviewed literature, the term “attention” is used to
describe two distinct concepts. The first concept is attention in terms of
models focusing on specific parts of input data through the use of auxiliary
information, such as attention maps obtained from domain experts [92, 24],
heatmaps generated by Grad-CAM [115], and segmenting disease-related regions
in the input [40, 59, 89]. The second concept refers to the attention-based
architectures, which are also the basis of Vision Transformers (ViT) [116]. We
further discuss these concepts in the following, and a summary of the reviewed
paper is presented in Table V.

Li et al. [92] proposed an attention-based CNN for glaucoma detection (AG-
CNN), which highlights salient regions by incorporating attention maps to
remove redundancy from fundus images. The proposed model comprises three
subnets: an attention prediction subnet, a pathological area localization
subnet and a glaucoma classification subnet. The attention maps in this paper
were collected in the large-scale attention-based glaucoma (LAG) database
through a simulated eye-tracking experiment. The ophthalmologists provided
attention maps by focusing on the salient regions of the fundus images. The
LAG database includes 5,824 fundus images labeled with either positive
glaucoma or negative glaucoma. They extended their work in [24] to enlarge the
LAG database to 11,760 fundus images. In addition to the supervised method
presented in [92], this paper further proposed a weakly supervised learning to
incorporate attention maps in a weakly supervised manner for glaucoma
detection. George et al. [115] proposed an end-to-end attention-guided 3D CNN
model for glaucoma detection and visual field index (VFI) estimation using
high-resolution 3D OCT volumes. The proposed model consists of three pathways
sharing the same network architecture. The first pathway takes the raw 3D-OCT
cube as input and learns global retinal structures relevant to glaucoma
detection and VFI estimation. Similarly, the inputs of the other two pathways
are computed during training, guided by the 3D Grad-CAM [117] attention
heatmaps. Each pathway outputs the class label, and the entire model is
trained concurrently to minimize the sum of the three losses. The final output
is obtained by fusing the predictions of the three pathways.

TABLE V: Comparison of papers utilizing attention-based architectures for
glaucoma classification. Studies with (*) mark tested their model on an
auxiliary dataset. N: Normal, G: Glaucoma, S: Glaucoma Suspect, A: Advanced
glaucoma, E: Early glaucoma, Avg: Average, Att: Attention module.

Study |  Model |  Data type |  Dataset | Classification Performance Measures (%)
---|---|---|---|---
ACC |  SEN |  SPE |  PRC |  F1-score |  AUC
[91]* |  CNN + Att |  Fundus |  LAG |  97.12 |  95.20 |  98.16 |  - |  95.47 |  99.28
REFUGE |  95.25 |  80.00 |  96.94 |  - |  78.82 |  93.32
RIM-ONE |  93.96 |  89.74 |  97.12 |  - |  90.91 |  97.19
[118] |  CNN + Att |  Fundus |  LAG |  97.12 |  97.21 |  97.07 |  - |  96.65 |  99.31
[119] |  CNN + Att |  Fundus |  LAG |  96.70 |  96.10 |  97.00 |  - |  95.00 |  99.60
[120] |  Swin [121] |  Fundus |  6 publicly available datasets‡ |  93.20 |  92.57 |  93.43 |  - |  - |  97.77
Cait [122] |  94.50 |  89.92 |  96.04 |  - |  - |  97.90
CrossViT [123] |  94.30 |  86.73 |  96.94 |  - |  - |  96.87
XciT [124] |  93.55 |  88.6 |  95.23 |  - |  - |  97.2
ResMlp [125] |  91.50 |  85.94 |  93.43 |  - |  - |  96.00
DeiT [126] |  88.00 |  81.70 |  90.10 |  - |  - |  94.60
ViT [116] |  87.40 |  77.20 |  90.60 |  - |  - |  92.60
|  BeiT [127] |  |  |  85.50 |  83.82 |  86.15 |  - |  - |  92.70
[128]* |  TIA-Net |  Fundus |  Private (1,005 N, 877 G) |  85.70 |  84.90 |  86.90 |  - |  - |  92.90
ORIGA |  76.60 |  75.30 |  77.20 |  - |  - |  83.50
[92]* |  AG-CNN |  Fundus |  LAG |  95.30 |  95.40 |  95.20 |  - |  95.10 |  97.50
RIM-ONE |  85.20 |  84.80 |  85.50 |  - |  83.70 |  91.60
[24] |  AG-CNN |  Fundus |  LAG |  92.20 |  95.40 |  96.70 |  - |  95.40 |  98.30
[129] |  CNN + Att |  OCT |  Private (90 N, 72 E, 57 A) |  87.88 (Avg‡‡) |  81.82 (Avg) |  90.91 (Avg) |  81.82 (Avg) |  81.82 (Avg) |  -
[18] |  DRT |  OCT, VF |  Private (697 G, 698 N) |  88.30 |  93.70 |  82.40 |  - |  88.90 |  93.90
[115] |  CNN + Grad-CAM |  OCT |  Private (427 N, 3,355 G) |  91.07 |  95.12 |  - |  94.73 |  94.88 |  93.77
‡ LAG, ODIR-5K, ORIGA, REFUGE, DRISHTI-GS1, HRF
‡‡ Average categorical results for discriminating between healthy, early and
advanced glaucoma samples.

García et al. [129] proposed a hybrid neural network with hand-driven features
and a deep learning backbone that has skip-connections to include tailored
residual and attention modules to refine the automatic features of the latent
space. They specifically fed the backbone model with raw OCT B-scans and used
a descriptor to extract RNFL thickness-based information as hand-driven
features. The model was trained using a few-shot learning technique for
discriminating between healthy, early and advanced glaucoma scans. Zhao et al.
[91] proposed a student-teacher framework where both the student and teacher
models have identical network architecture consisting of two separate
attention pathways (i.e., spatial and channel attention modules). The spatial
attention module identifies salient image regions and prune feature responses,
while the channel attention module identifies salient feature channels to
preserve the activations relevant to the glaucoma diagnosis task. The proposed
framework aims to improve glaucoma diagnosis on imbalanced data by augmenting
feature distribution with feature distilling and re-weighting. Guo et al.
[119] proposed a multitask teacher-student framework for unbiased glaucoma
screenings and visualizations of model decision-making areas. The teacher
network in the proposed framework utilizes a ResNet-34 backbone to extract
semantic feature maps of different depths for constructing a multi-scale
discrimination module and adopts the self-attention mechanism module to make
the network pay attention to spatial information and channel information at
the same time. This ensures the quality of the generated evidence map and
provides reliable preliminary results for glaucoma classification. Meanwhile,
the student network, incorporating a dual-branch CNN structure and a
collaborative learning module, simultaneously performs glaucoma diagnosis and
generates the corresponding evidence map.

The ViT is a deep learning architecture that utilizes a self-attention
mechanism to capture the global characteristics of an image. ViT applies the
transformer model, which was originally designed for natural language
processing, to computer vision tasks [116]. Wassel et al. [120] evaluated the
performance of more than seven different ViT baseline models for glaucoma
detection on a combined dataset of six publicly available fundus images. They
also proposed an ensemble of the best ViT models. Swin [121] achieved the best
standalone performance with 92.57% in sensitivity, 96.94% in specificity, and
97.90% in AUC. Xu et al. [128] proposed a Transfer Induced Attention Network
(TIA-Net) for automatic glaucoma detection. TIA-Net leverages the fundus
feature learned from similar ophthalmic data to extract general features. The
channel-wise attention and maximum mean discrepancy are then adopted to
extract the discriminative features that fully characterize the glaucoma-
related deep patterns. As a result, the proposed method achieves a smooth
transition between general and specific features, thus enhancing feature
transferability. Song et al. [18] proposed a Deep Relation Transformer (DRT)
method for diagnosing glaucoma based on the combined OCT and VF modalities.
The proposed framework includes three successive modules: the global relation
module, the guided regional relation module, and the interaction transformer
module. These modules utilize deep reasoning and transformer mechanisms to
explore implicit pairwise relations between OCT and VF information and enhance
the representation with complementary information. The proposed DRT approach
outperforms existing methods and has the potential to accurately diagnose
glaucoma using multimodal data.

###  6.4 Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a type of deep neural network
architecture that has the ability to generate new samples from a given
probability distribution [130]. The GANs consist of two parts: the generator
and the discriminator. The generator is trained to produce realistic samples,
while the discriminator is trained to differentiate between synthetic data and
real data. Diaz-Pinto et al. [131] proposed a framework for glaucoma
assessment by developing a fundus image synthesizer and a semi-supervised
learning method using the Deep Convolutional Generative Adversarial Network
(DCGAN) [132] architecture. The architecture of the image synthesizer and
semi-supervised learning method are identical, except for the last output
layer of the discriminator. The DCGAN discriminator is modified in the semi-
supervised learning method to function as a 3-class classifier capable of
distinguishing between Normal, Glaucoma, and Real/Fake classes. The models
were trained on 86,926 cropped retinal images obtained from fourteen publicly
available databases.

Guo et al. [93] proposed the use of CycleGAN [133] in a teacher-student
framework to reduce the appearance differences between labeled source domain
and labeled target domain images, aiming to enhance the accuracy of
multiracial glaucoma detection. The proposed framework consists of an inter-
image tutor, an intra-image tutor, a student model and a backbone network,
which combines the advantages of domain adaptation and semi-supervised
learning. The inter-image tutor uses CycleGAN for style transfer and transfers
the learned knowledge to the student model by minimizing knowledge
distillation loss. This helps to overcome the domain shift problem and improve
the performance of glaucoma detection. The intra-image tutor adopts the
exponential moving average to leverage the unlabeled target domain and
transfers the knowledge to the student model by minimizing prediction
consistency loss. The student model not only directly learns knowledge from
the labeled target domain images, but also learns the intra-image knowledge
and inter-image knowledge transfer by two tutors. Furthermore, the backbone
integrates the context features of the local OD region and global fundus image
via modified ResNet-50. Comprehensive experimental results on various datasets
are shown in Table VI.

TABLE VI: Comparison of papers utilizing GAN-based architectures for glaucoma
classification. Studies with (*) mark tested their model on an auxiliary
dataset

Study |  Model |  Data type |  Dataset | Classification Performance Measures (%) |  Code
---|---|---|---|---|---
ACC |  SEN |  SPE |  F1-score |  AUC
[131] |  DCGAN |  Fundus |  14 publicly available datasets‡ |  - |  82.90 |  79.86 |  84.29 |  90.17 |  Code
[93]* |  CycleGAN-based |  Fundus |  LAG |  98.14 |  98.62 |  98.17 |  - |  96.41 |  -
REFUGE |  98.74 |  96.85 |  96.57 |  - |  97.06
ORIGA |  97.64 |  97.13 |  97.62 |  - |  96.93
DRISHTI-GS |  98.45 |  97.66 |  96.84 |  - |  97.04
ACRIMA |  96.52 |  97.62 |  96.72 |  - |  97.26
RIM-ONE-r1 |  97.48 |  98.31 |  97.14 |  - |  97.60
RIM-ONE-r2 |  97.46 |  98.23 |  96.57 |  - |  96.40
RIM-ONE-r3 |  96.46 |  97.18 |  96.82 |  - |  96.56
‡ ORIGA-light, DRISHTI-GS1, RIM-ONE, sjchoi86-HRF, HRF, DRIVE [134], MESSIDOR
[135], DR KAGGLE [136], STARE [137], e-ophtha [138], ONHSD [139], CHASEDB1
[140], DRIONS-DB, SASTRA [141], ACRIMA

###  6.5 Geometric Deep Learning Networks

Geometric deep learning is a machine learning approach that focuses on
developing algorithms and network architectures capable of analyzing non-
Euclidean structured data, such as graphs, manifolds, and point clouds, by
integrating geometric principles and deep learning techniques [142]. Thiéry et
al. [143] proposed using geometric deep learning to diagnose glaucoma from a
single OCT scan of the ONH and compared its performance to that of 3D CNN and
RNFL thickness. Using a deep learning model, they first segmented seven major
neural and connective tissues from OCT images. After that, each ONH was
represented as a 3D point cloud with approximately 1,000 points. Geometric
deep learning (PointNet [144]) was then used to diagnose glaucoma from a
single 3D point cloud. The proposed geometric deep learning model achieved an
AUC of 95.00% on a private dataset consisting of 873 glaucomatous and 3,897
non-glaucomatous OCT scans, outperforming that obtained with a 3D CNN (AUC of
87.00% on raw OCT images and 91.00% on segmented OCT images) and that obtained
from RNFL thickness alone (AUC = 80.00%). In another study, Braeu et al. [145]
proposed to compare the performance of PointNet with dynamic graph
convolutional neural network (DGCNN) [146] for diagnosing glaucoma. Following
the same procedure as their previous paper [143], each ONH was represented as
a 3D point cloud and used to diagnose glaucoma. They demonstrated that both
the DGCNN and PointNet could accurately classify 2,259 glaucomatous from 2,247
Non-glaucomatous OCT scans based on 3D ONH point clouds with an AUC of 97.00%
and 95.00%, respectively. Furthermore, they identified critical 3D structural
features of the ONH for glaucoma diagnosis, which formed an hourglass pattern
mostly located within the NRR in the inferior and superior quadrants of the
ONH.

###  6.6 Hybrid Networks

Hybrid networks can help in glaucoma diagnosis by combining the strengths of
different types of neural networks. For example, Chai et al. [19] designed a
multi-branch neural network (MB-NN) model to exploit domain knowledge and
extract hidden features from retinal fundus images. The entire fundus image
and the OD region image are the inputs to the first and second branches of the
MB-NN model, respectively. The OD region was extracted using Faster-RCNN [54]
trained on a separate dataset. Similarly, they integrated domain knowledge
into a one-dimensional feature vector and fed it into the third branch of the
proposed model. Fu et al. [147] also proposed a disc-aware ensemble network
(DENet) for automatic glaucoma screening that integrates the deep hierarchical
context of the global fundus image and the local OD region. The network
consists of four deep streams, including a global image stream, a
segmentation-guided network, a local Disc region stream, and a Disc polar
transformation stream. The segmentation-guided network is based on U-shape
convolutional network to detect the OD region and guide glaucoma screening on
the whole fundus image. The architecture of the other streams is based on the
ResNet-50 model. These streams provide complementary information, and their
output probabilities are fused to obtain the final classification result. Yu
et al. [148] proposed using raw multi-rater gradings to improve the
performance of deep learning models for glaucoma classification. Instead of
predicting labels from individual raters, the authors proposed a multi-branch
structure that generates three predictions with different sensitivity settings
for the input images: one with the best sensitivity, one with the best
specificity, and one with a balanced fused result. A consensus loss is
introduced to encourage consistent results from the sensitivity and
specificity branches for consensus labels and opposite results for
disagreement labels. Meanwhile, the consistency or inconsistency between the
prediction results of the two branches is used to determine the difficulty
level of an image, which is further used to guide the balanced fusion branch
to focus more on hard cases. García et al. [149] proposed combining CNN and
LSTM networks for glaucoma diagnosis from raw SD-OCT volumes. The proposed
model consists of a slide-level feature extractor and a volume-based
predictive model. The feature extractor utilized residual and attention
convolutional modules combined with fine-tuning techniques. Also, to
incorporate spatial information along with the three-dimensional data, the
model used the LSTM networks with a sequential-weighting module. This module
helps to optimize the LSTM outputs, resulting in a more stable and efficient
model learning process.

TABLE VII: Comparison of papers utilizing hybrid architectures for glaucoma
classification. Studies with (*) mark tested their model on an auxiliary
dataset. N: Normal, G: Glaucoma, SINDI: Singapore Indian Eye Study.

Study |  Model |  Data type |  Dataset | Classification Performance Measures (%) |  Code
---|---|---|---|---|---
ACC |  SEN |  SPE |  F1-score |  AUC
[147]* |  DENet |  Fundus |  ORIGA, SCES (46 G, 1,630 N) |  84.29 |  84.78 |  83.80 |  - |  91.83 |  Code
SINDI (113 G, 5,670 N) |  74.95 |  78.76 |  71.15 |  - |  83.80
[19] |  MB-NN |  Fundus |  Private (1,023 G, 1,531 N) |  91.51 |  92.33 |  90.90 |  - |  - |  -
[148]* |  Multi-rater deep model |  Fundus |  Private (2,952 G, 3,366 N) |  92.54 |  90.96 |  93.92 |  91.89 |  97.94 |  -
DRISHTI-GS1 |  86.14 |  91.43 |  74.19 |  90.14 |  89.63 |  -
REFUGE |  98.00 |  82.50 |  99.72 |  - |  96.83 |  -
[149] |  CNN + LSTM |  OCT |  Private (144 G, 176 N) |  81.25 |  75.86 |  85.71 |  78.57 |  80.79 |  -

##  7 Glaucoma Prediction

Glaucoma is a degenerative disease that often exhibits minimal symptoms during
its initial stages [150]. Therefore, predicting the risk of developing
glaucoma is crucial for early detection and timely intervention to prevent
vision loss. Recent studies have demonstrated the potential of deep learning
models in predicting the probability of glaucoma development through the
analysis of diverse demographic, clinical, and imaging data [150, 151, 22]. Li
et al. [150], for example, proposed to use a CNN-based network to predict and
stratify the risk of glaucoma onset and progression based on fundus images of
17,497 eyes in 9,346 patients. The model demonstrated the ability to predict
patients who may develop glaucoma within a five-year period with an AUC of
90.00%. Thakur et al. [151] also used deep learning models to predict glaucoma
development from fundus images in a prospective longitudinal study several
years before disease onset. The study reported an AUC of 77.00% for predicting
the development of glaucoma 4 to 7 years before the onset of the disease,
88.00% for predicting the development of glaucoma approximately 1 to 3 years
before the onset, and 95.00% for detecting glaucoma after the onset.

Li et al. [31] proposed a deep learning model for glaucoma prediction (DeepGF)
based on sequential fundus images. They first established a database of
sequential fundus images for glaucoma prediction (SIGF), which included an
average of 9 images per eye, for a total of 3,671 images. The proposed DeepGF
consists of an attention-polar convolution neural network and a variable time
interval long short-term memory (VTI-LSTM) network to learn the spatio-
temporal transition at different time intervals across sequential medical
images of a person. In addition, a novel active convergence training strategy
is proposed to solve the glaucoma forecast’s imbalanced sample distribution
problem. The proposed method demonstrated an accuracy of 80.7%, a sensitivity
of 85.70%, a specificity of 80.60%, and an AUC of 87.00%. Dixit et al. [22]
proposed using a convolutional LSTM neural network to detect glaucoma
progression from a longitudinal dataset of merged VF and clinical data. The
dataset comprises 11,242 eyes with at least four VF results and corresponding
baseline clinical data, including CDR, central corneal thickness, and IOP for
each sample. The proposed method achieved an AUC of 93.93% for glaucoma
progression prediction. [152] developed a deep archetypal framework to predict
glaucoma approximately four years prior to disease onset. The framework
utilizes simplex projections to obtain unsupervised convex representations of
the visual fields, which proved clinically meaningful and more discriminative
than raw VFs or other classical VF analysis approaches. The proposed model
achieved an AUC of 71.00% on a dataset of 7,248 VF tests collected only at the
baseline.

##  8 Challenges and Future Directions

The adoption of deep learning for glaucoma diagnosis faces several key
challenges that need to be addressed through continued research and cross-
disciplinary collaboration. We structure these challenges and promising future
directions as follows.

###  8.1 Data Challenges

Developing effective deep learning models for glaucoma diagnosis relies on the
availability of comprehensive training datasets. However, assembling high-
quality annotated datasets presents several key challenges.

One major challenge is the limited size of publicly available glaucoma
datasets compared to common computer vision benchmarks like ImageNet. The
prohibitive expertise and cost required for reliable manual annotations
further constrains dataset development. Data diversity is another issue, with
many datasets lacking varied representations across different demographics,
ethnicities, and imaging equipment [153, 154]. These limitations have caused
existing public datasets to remain relatively small in scale, which can hinder
model performance and restrict real-world applicability. However, alternative
training methods such as transfer learning [128, 48], zero/few-shot learning
[129, 155], and knowledge distillation [156, 91, 119] offer potential
solutions to mitigate the impact of limited dataset size. Additionally, active
learning techniques can optimize the annotation process by selectively
identifying the most useful and ambiguous samples for labeling. Incorporating
uncertainty estimation into the sample selection process [157] further focuses
active learning on improving model performance on underrepresented classes.

Imbalanced class distribution poses another persistent problem, with normal
cases dominating many current datasets. This can skew model performance toward
low sensitivity and high false negative rates [158]. Techniques like data
augmentation [159, 160], weighted sampling [161], and generation of synthetic
minority over-samples [162] may help mitigate imbalance. However, augmentation
requires judicious implementation with clinician input to prevent medical
inaccuracies and ensure fidelity [163].

Healthcare data also carries ethical and legal obligations around privacy that
researchers must proactively address [164]. Laws and cultural norms around
medical data sharing vary geographically, necessitating localized
considerations. Policymakers have a role in developing balanced frameworks
that promote research and innovation while protecting patient privacy.
Techniques like federated learning [165] can also enable collaborative model
development without raw data sharing. However, the onus remains on researchers
to be privacy stewards and ensure compliance with applicable national and
regional privacy laws in their research studies [166].

###  8.2 Model Development Challenges

Deep learning models need to detect subtle signs of early-stage glaucoma,
which is challenging even for experienced clinicians [167]. Heterogeneity in
early disease appearance further complicates this task. Researchers are
exploring various model architecture techniques to improve performance for
early detection. For instance, attention mechanisms have shown promise in
focusing models on salient retinal regions, enhancing informative feature
extraction, and representation learning [24].

Integrating multi-modal data and multi-label learning may further improve
performance by leveraging interrelated tasks like segmentation and diagnosis
[18]. However, this poses challenges such as optimal data fusion, balancing
diverse tasks, and continual learning as new data emerges. To address these
issues, researchers are exploring specialized multi-modal architectures,
adaptive optimization strategies, dynamic network expansion, and meta-learning
[168].

Despite the potential benefits, the opaque nature of deep learning models
remains a challenge. The importance of employing or proposing explainable AI
(XAI) techniques to explain predictions and feature importance from deep
learning models using different image modalities like OCT, Fundus, and VF has
been recognized by researchers [106, 169, 94]. These studies employed post-hoc
explanation techniques like SHAP values [170], locally interpretable model-
agnostic explanations [171], integrated gradients [172], occlusion sensitivity
[173], saliency maps [174], and contrastive explanations [175] to identify
influential segments, patterns, and abnormalities in fundus images that lead
the models to predict glaucoma. They found that XAI methods can highlight
relevant regions like the optic disc, retinal nerve fiber layer defects, and
areas of hemorrhaging that most inform the models’ predictions [176, 177].
However, further development of XAI techniques tailored for glaucoma is still
necessary, as most methods are model-agnostic and not optimized for
glaucomatous features. Most work has focused on post-hoc methods rather than
real-time explainability during model development and evaluation. To enhance
understanding and trust in AI-assisted glaucoma screening, the field needs to
create glaucoma-specific XAI solutions that provide human-interpretable
explanations in real time. Another pragmatic approach involves clinician-
researcher collaboration to assess XAI techniques and identify medically
relevant insights [108].

###  8.3 Clinical Translation Challenges

For clinical adoption, glaucoma diagnosis models must demonstrate consistent
performance across diverse populations and imaging equipment. However,
demographic and acquisition differences can affect model generalizability.
Wider collaborations between academia, healthcare providers, and industry
could facilitate large-scale external validation across multiple centers and
populations to identify failure modes. The models should integrate seamlessly
into ophthalmic workflows with efficient computations on edge devices [178].
Physician trust is integral for uptake, requiring initiatives to improve model
explainability and transparency. Frameworks for uncertainty estimation could
also indicate situations where clinician oversight is necessary.

Careful design of human-AI interaction mechanisms will be vital, allowing
physicians to accept, reject or modify model recommendations. Regulatory
agencies play a crucial role in establishing standards for rigorous clinical
validation of deep learning systems before approval [179]. Beyond accuracy,
aspects like usability, interoperability, cybersecurity and patient privacy
must be addressed to ensure safety and effectiveness [180]. Overall, a
patient-centered approach with clinician partnership in the model development
lifecycle will be key for clinical translation.

###  8.4 Future Outlook

Advancements in glaucoma diagnosis will require synergistic progress across
multiple disciplines. On the data front, innovations in sensor and imaging
technologies can enable the acquisition of informative multi-modal datasets.
In parallel, advancements in deep learning architectures, optimization
algorithms, and computing hardware will allow more sophisticated analysis of
these rich datasets.

Several promising research directions could accelerate translating of these
innovations into clinical practice. Meta-learning approaches may enable rapid
model adaptation from limited annotated examples, mitigating data constraints.
Adversarial techniques can improve model robustness to input perturbations.
Reinforcement learning offers the potential for optimizing glaucoma management
policies. Recurrent neural networks could integrate longitudinal patient data
for enhanced monitoring.

Ultimately, sustained collaborative efforts spanning medicine, engineering and
computer science will be key to realizing artificial intelligence’s potential
for enhancing glaucoma outcomes. Initiatives to promote open datasets, model
repositories and evaluation benchmarks will facilitate collective progress.
Interdisciplinary teams should lead technological development, ensuring
clinical applicability and integration into practice. With concerted efforts,
AI-enabled glaucoma care could soon transition from promise to reality.

##  9 Conclusion

In this paper, we have provided a comprehensive overview of the state-of-the-
art research applying deep learning and computer vision techniques for
glaucoma diagnosis. Through a systematic literature review methodology, we
synthesized the existing work across diverse architectural categories,
including CNNs, autoencoders, attention networks, GANs, and geometric deep
learning models.

The review highlighted promising capabilities demonstrated by these techniques
in analyzing fundus, OCT, and visual field data. Tasks like classification,
segmentation, and prediction of glaucoma have shown strong results across a
wide range of experiments. We also discussed different feature extraction
approaches, covering structural, statistical, and hybrid techniques for
identifying informative glaucoma biomarkers from retinal imaging data.
However, key challenges remain around limited dataset size and diversity,
class imbalance, optimizing models for early disease detection, integrating
multi-modal data, and translating solutions to clinical practice.

Ongoing efforts are beginning to address these gaps through transfer learning
approaches, data augmentation techniques, attention mechanisms, multi-task
learning, model explainability, and physician collaboration. Nonetheless,
realizing the full potential of AI in transforming glaucoma care will require
sustained cross-disciplinary teamwork.

From our perspective, open datasets, model repositories, and evaluation
benchmarks will be critical to accelerate collective progress. Ultimately, an
integrated approach spanning medicine, engineering and computer science will
be essential for developing and validating solutions ready for real-world
clinical deployment. This review highlights the tremendous opportunities for
us at the intersection of ophthalmology and artificial intelligence. Overall,
we aimed to provide a comprehensive overview and analysis of the state-of-the-
art in this exciting and high-impact emerging field.

## Acknowledgment

Funding: This work was supported in part by the European Research Council
(ERC) through the Horizon 2020 research and innovation program under grant
agreement number 101002711. Mohammad Mahdi Dehshibi received partial funding
from this source.

Author Contributions: All authors contributed to the conception of the idea,
defined the scope of the survey, and developed the survey methodology. Mona
Ashtari-Majlan conducted the literature review, identified research gaps,
analyzed survey data, and wrote the paper. All authors participated in the
discussion of results, provided feedback on the manuscript, and assisted in
writing and editing.

Competing Interests: The authors declare no competing interests.

Data and Materials Availability: All data presented in this study is available
within the main text.

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|  Mona Ashtari-Majlan received her Master’s degree in Health Systems
Engineering from Amirkabir University of Technology, Tehran, in 2021. She is
currently a Ph.D. candidate in the Doctoral program in Network and Information
Technologies at Universitat Oberta de Catalunya, Spain. Her research interests
include Biomedical Image Processing, Computer Vision, and Deep Learning.
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|  Mohammad Mahdi Dehshibi received his Ph.D. in Computer Science in 2017. He
is currently a research scientist at Universidad Carlos III de Madrid, Spain.
He is also an adjunct researcher at Universitat Oberta de Catalunya (Spain)
and the Unconventional Computing Lab. at UWE (Bristol, UK). He has contributed
to more than 70 papers published in peer-reviewed journals and conference
proceedings. He also serves as an associate editor of the _International
Journal of Parallel, Emergent, and Distributed Systems_. His research
interests include Deep Learning, Medical Image Processing, Affective
Computing, and Unconventional Computing.
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|  David Masip (Senior Member, IEEE) received his Ph.D. degree in Computer
Vision in 2005 (Universitat Autonoma de Barcelona, Spain). He was awarded for
the best thesis in Computer Science. He is a Full Professor at the Computer
Science Multimedia and Telecommunications Department at Universitat Oberta de
Catalunya, Spain, and the Director of the Doctoral School since 2015. He has
published more than 70 scientific papers in relevant journals and conferences.
His research interests include Oculomics, Retina Image Analysis and Affective
Computing.
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