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.
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24 July 2024
AICTE & DSIR SPONSORED INTERNATIONAL CONFERENCE ON SMART AND INNOVATIVE DEVELOPMENT IN SCIENCE, ENGINEERING & TECHNOLOGY 2023
16–18 August 2023
Bhilai, India
Research Article|
July 24 2024
Diabetic Macular Edema severity evaluation using PG-GAN and ResNet
Vaishnavi Deshpande;
Vaishnavi Deshpande
a)
1
Department of Electronics and Telecommunication Engineering COEP Technological University
, Pune, Maharashtra 411 005, India
.a)Corresponding author: [email protected]
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Vishal Raner;
Vishal Raner
b)
2
Department of Computer Science and Engineering COEP Technological University
, Pune, Maharashtra 411 005, India
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Amit Joshi
Amit Joshi
c)
2
Department of Computer Science and Engineering COEP Technological University
, Pune, Maharashtra 411 005, India
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AIP Conf. Proc. 3111, 020005 (2024)
Citation
Vaishnavi Deshpande, Vishal Raner, Amit Joshi; Diabetic Macular Edema severity evaluation using PG-GAN and ResNet. AIP Conf. Proc. 24 July 2024; 3111 (1): 020005. https://doi.org/10.1063/5.0221716
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