Ophthalmology extensively relies on the application of Artificial Intelligence and Deep Learning techniques to address various challenges and issues. Macula is the central part of the retina responsible for sharp, detailed vision. Macular edema is a condition that affects the macula. It occurs when fluid accumulates in the macula, leading to swelling and distortion of the central vision system. Macular Edema can be detected using computer vision and Artificial Intelligence techniques. Large datasets having high-resolution images are required for building robust Deep Learning medical image classification models. Access to large, annotated, and readily available datasets is often limited, as the preparation of such dataset is a costly and tedious job. Leveraging a mixture of publicly accessible datasets for retinal fundus images is one strategy that can be employed. However, this solution is accompanied by the challenges pertaining to the quality, reliability, and lack of uniformity across the datasets. The complex architectures of Deep Learning models are not well suited for medical image classification using smaller datasets. This is because the model over fits during training. This work proposes a robust ResNet-50 classifier model that is trained on synthetic retinal fundus images generated by the Progressive Growing of Generative Adversarial Network. The model classifies retinal fundus images based on the severity grade of macular edema. The ResNet-50 model trained on the original IDRiD dataset achieved an accuracy of 85% while it is improved to 90.82% using Progressive Growing of Generative Adversarial Network based approach.

1.
Feng
H.
,
Chen
J.
,
Zhang
Z.
,
Lou
Y.
,
Zhang
S.
,
Yang
W.
A bibliometric analysis of artificial intelligence applications in macular edema: exploring research hotspots and Frontiers
.
Frontiers in Cell and Developmental Biology.
2023
May 15;
11
:
1174936
.
2.
Hee
M.R.
,
Puliafito
C.A.
,
Wong
C.
,
Duker
J.S.
,
Reichel
E.
,
Rutledge
B.
,
Schuman
J.S.
,
Swanson
E.A.
,
Fujimoto
J.G.
Quantitative assessment of macular edema with optical coherence tomography
.
Archives of ophthalmology.
1995
Aug 1;
113
(
8
):
1019
29
.
3.
Aljameel
S.S.
A Proactive Explainable Artificial Neural Network Model for the Early Diagnosis of Thyroid Cancer
.
Computation.
2022
Oct 11;
10
(
10
):
183
.
4.
Desideri
L.F.
,
Rutigliani
C.
,
Corazza
P.
,
Nastasi
A.
,
Roda
M.
,
Nicolo
M.
,
Traverso
C.E.
,
Vagge
A.
The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases
.
Journal of Optometry.
2022
Jan 1;
15
:
S50
7
.
5.
Ahuja
A.S.
The impact of artificial intelligence in medicine on the future role of the physician
.
PeerJ.
2019
Oct 4;
7
:
e7702
.
6.
Sawhney
R.
,
Malik
A.
,
Sharma
S.
,
Narayan
V.
A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease
.
Decision Analytics Journal.
2023
Mar 1;
6
:
100169
.
7.
Hunt
B.R.
Super-resolution of images: Algorithms, principles, performance
. in
ternational Journal of Imaging Systems and Technology.
1995
Dec;
6
(
4
):
297
304
.
8.
David
D.S.
,
Selvi
S.
,
Sivaprakash
S.
,
Raja
P.V.
,
Sharma
D.K.
,
Dadheech
P.
,
Sengan
S.
Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics
.
Computers, Materials & Continua.
2022
Feb 1;
70
(
2
).
9.
Wei
M.
,
Huang
X.
,
Han
W.
,
Tian
Z.
,
Wu
G.
,
Wang
S.
,
Sun
L.
Stain-free Holographic Detection of Circulating Tumor Cells Using A Deep Feature Fusion Neural Network
. In
2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS)
2022
Jun 13 (pp.
102
105
). IEEE.
10.
Wang
T.
,
Lei
Y.
,
Fu
Y.
,
Wynne
J.F.
,
Curran
W.J.
,
Liu
T.
,
Yang
X.
A review on medical imaging synthesis using deep learning and its clinical applications
.
Journal of applied clinical medical physics.
2021
Jan;
22
(
1
):
11
36
.
11.
Xu
W.
,
Fu
Y.L.
,
Zhu
D.
ResNet and Its Application to Medical Image Processing: Research Progress and Challenges
.
Computer Methods and Programs in Biomedicine.
2023
Jun 8:
107660
.
12.
Karras
T.
,
Aila
T.
,
Laine
S.
,
Lehtinen
J.
Progressive growing of gans for improved quality, stability, and variation
. arXiv preprint arXiv:1710.10196.
2017
Oct 27.
13.
Wang
T.
,
Lei
Y.
,
Fu
Y.
,
Curran
W.J.
,
Liu
T.
,
Yang
X.
Medical imaging synthesis using deep learning and its clinical applications: A review
. arXiv preprint arXiv:2004.10322.
2020
Apr 21.
14.
Xu
W.
,
Fu
Y.L.
,
Zhu
D.
ResNet and Its Application to Medical Image Processing: Research Progress and Challenges
.
Computer Methods and Programs in Biomedicine.
2023
Jun 8:
107660
.
15.
Kumar
Y.
,
Gupta
S.
Deep transfer learning approaches to predict glaucoma, cataract, choroidal neovascularization, diabetic macular edema, drusen and healthy eyes: an experimental review
.
Archives of Computational Methods in Engineering.
2023
Jan;
30
(
1
):
521
41
.
16.
Guergueb
T.
,
Akhloufi
M.A.
A Review of Deep Learning Techniques for Glaucoma Detection
.
SN Computer Science.
2023
Mar 23;
4
(
3
):
274
.
17.
Li
F.
,
Wang
Y.
,
Xu
T.
,
Dong
L.
,
Yan
L.
,
Jiang
M.
,
Zhang
X.
,
Jiang
H.
,
Wu
Z.
,
Zou
H.
Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs
.
Eye.
2022
Jul;
36
(
7
):
1433
41
.
18.
Benvenuto
D.
,
Giovanetti
M.
,
Vassallo
L.
,
Angeletti
S.
,
Ciccozzi
M.
Application of the ARIMA model on the COVID-2019 epidemic dataset
.
Data in brief.
2020
Apr 1;\v29:
105340
.
19.
Camara
J.
,
Neto
A.
,
Pires
I.M.
,
Villasana
M.V.
,
Zdravevski
E.
,
Cunha
A.
Literature review on artificial intelligence methods for glaucoma screening, segmentation, and classification
.
Journal of Imaging.
2022
Jan 20;
8
(
2
):
19
.
20.
Li
F.
,
Wang
Y.
,
Xu
T.
,
Dong
L.
,
Yan
L.
,
Jiang
M.
,
Zhang
X.
,
Jiang
H.
,
Wu
Z.
,
Zou
H.
Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs
.
Eye.
2022
Jul;
36
(
7
):
1433
41
.
21.
Kumar
Y.
,
Gupta
S.
Deep transfer learning approaches to predict glaucoma, cataract, choroidal neovascularization, diabetic macular edema, drusen and healthy eyes: an experimental review
.
Archives of Computational Methods in Engineering.
2023
Jan;
30
(
1
):
521
41
.
22.
Suganyadevi
S.
,
Seethalakshmi
V.
,
Balasamy
K.
A review on deep learning in medical image analysis
. in
ternational Journal of Multimedia Information Retrieval.
2022
Mar;
11
(
1
):
19
38
.
23.
Kumar
A.
,
Tewari
A.S.
,
Singh
J.P.
Classification of diabetic macular edema severity using deep learning technique
.
Research on Biomedical Engineering.
2022
Sep;
38
(
3
):
977
87
.
24.
Kokate
P.
,
Joshi
A.D.
,
Tamizharasan
P.S.
An empirical comparison of generative adversarial network (gan) measures
. In
Advances in Communication and Computational Technology: Select Proceedings of ICACCT 2019
2021
(pp.
1383
1396
).
Springer
Singapore
.
25.
Suganyadevi
S.
,
Seethalakshmi
V.
,
Balasamy
K.
A review on deep learning in medical image analysis
. in
ternational Journal of Multimedia Information Retrieval.
2022
Mar;
11
(
1
):
19
38
.
26.
Skandarani
Y.
,
Jodoin
P.M.
,
Lalande
A.
Gans for medical image synthesis: An empirical study
.
Journal of Imaging.
2023
Mar 16;
9
(
3
):
69
.
27.
Iqbal
T.
,
Ali
H.
Generative adversarial network for medical images (MI-GAN
).
Journal of medical systems.
2018
Nov;
42
:
1
1
.
28.
Bissoto
A.
,
Valle
E.
,
Avila
S.
Gan-based data augmentation and anonymization for skin-lesion analysis: A critical review
. in
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
2021
(pp.
1847
1856
).
29.
Andreini
P.
,
Bonechi
S.
,
Bianchini
M.
,
Mecocci
A.
,
Scarselli
F.
Image generation by GAN and style transfer for agar plate image segmentation
.
Computer methods and programs in biomedicine.
2020
Feb 1;
184
:
105268
.
30.
Sun
L.
,
Wang
J.
,
Huang
Y.
,
Ding
X.
,
Greenspan
H.
,
Paisley
J.
An adversarial learning approach to medical image synthesis for lesion detection
.
IEEE journal of biomedical and health informatics.
2020
Jan 6;
24
(
8
):
2303
14
.
31.
Devi
Y.S.
,
Kumar
S.P.
DR-DCGAN: A Deep Convolutional Generative Adversarial Network (DC-GAN) for Diabetic Retinopathy Image Synthesis
.
Webology
(ISSN: 1735-188X).
2022
;
19
(
2
).
32.
Beers
A.
,
Brown
J.
,
Chang
K.
,
Campbell
J.P.
,
Ostmo
S.
,
Chiang
M.F.
,
Kalpathy-Cramer
J.
High-resolution medical image synthesis using progressively grown generative adversarial networks
. arXiv preprint arXiv:1805.03144.
2018
May 8.
33.
Karras
T.
,
Aila
T.
,
Laine
S.
,
Lehtinen
J.
Progressive growing of gans for improved quality, stability, and variation
. arXiv preprint arXiv:1710.10196.
2017
Oct 27.
34.
Prasanna
Porwal
,
Samiksha
Pachade
,
Ravi
Kamble
,
Manesh
Kokare
,
Girish
Deshmukh
,
Vivek
Sahasrabuddhe
,
Fabrice Meriaudeau. Indian Diabetic Retinopathy Image Dataset (IDRiD) [Internet]
.
IEEE Dataport
;
2018
. Available from:
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