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Comparative Analysis of Diabetic Retinopathy Classification Approaches Using Machine Learning and Deep Learning Techniques

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Abstract

Diabetic retinopathy (DR) is an eye disease caused due to excess of sugar in retinal blood vessels and obstructs vision. Regular and timely diagnosis can prevent the severity of diabetic retinopathy at an initial stage. Manual diagnosis of diabetic retinopathy is time-consuming and thus a plethora of work has been done by researchers to automate the classification of diabetic retinopathy using machine learning and deep learning techniques. The present review pivots around the research papers covering recent and effective automated DR classification techniques from 2011 to 2022. A comparative analysis of these papers highlights the summary of DR classification datasets, pre-processing techniques, various advanced classification algorithms, and their performance. Along with the summary and analysis of the classification techniques, the present paper demonstrates the experimentation of eight pre-trained convolution neural network models on DR benchmark classification datasets. This is to help the researchers to choose the best pre-trained classification model for the corresponding DR dataset. The use of deep learning and machine learning algorithms demonstrated excellent performance; however, researchers still need to explore the design constraints of the classification models to have effective results. Attention mechanisms and vision transformers are recent breakthroughs that can be used to solve classification challenges. The objective of this article is to provide a single platform for researchers to access state-of-the-art work in the classification of diabetic retinopathy, the results of various pre-trained models on DR benchmark classification datasets, and future research prospects for the researchers working in this challenging area.

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  1. Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi, 110006, India

    Ruchika Bala & Arun Sharma

  2. Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, 110006, India

    Nidhi Goel

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RB: Study conception and design, Material preparation, data collection and analysis, First draft, Final approval. AS: Study conception and design, Material preparation, data collection and analysis, Review, Final approval. NG: Study conception and design, Material preparation, data collection and analysis, Review, Final approval.

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Bala, R., Sharma, A. & Goel, N. Comparative Analysis of Diabetic Retinopathy Classification Approaches Using Machine Learning and Deep Learning Techniques. Arch Computat Methods Eng 31, 919–955 (2024). https://doi.org/10.1007/s11831-023-10002-5

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