Advanced International Journal for Research

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Review on Deep Vision for Early Detection of Diabetic Retinopathy Using Machine Learning

Author(s) Sneha Shamrao Tidke, Prof. Rahul Bhandekar, Prof. Naina Junnake
Country India
Abstract Diabetic Retinopathy (DR) is one of the most severe microvascular complications of diabetes mellitus and a leadingcause of preventable blindness worldwide. Early diagnosis and timely treatment are critical, as DR often progressesasymptomatically until irreversible retinal damage occurs. Conventional diagnosis relies on manual examination of retinalfundus images by ophthalmologists, which is time-consuming, costly, and subject to inter-observer variability. Recentadvancements in computer-aided diagnosis have demonstrated that machine learning, particularly deep learning, cansignificantly enhance the accuracy and efficiency of DR detection. Convolutional Neural Networks (CNNs) have emerged asthe most effective approach for automated analysis of fundus images due to their capability to learn hierarchical anddiscriminative features directly from raw data. This review presents a comprehensive analysis of recent deep learning–basedmethods for diabetic retinopathy detection, classification, and severity grading. Key aspects such as preprocessing techniques,network architectures, publicly available datasets, and evaluation metrics are discussed. Furthermore, existing challengesincluding dataset imbalance, lack of interpretability, and limited clinical deployment are highlighted. The review alsoidentifies emerging trends and future research directions aimed at developing robust, explainable, and scalable DR screeningsystems suitable for real-world clinical applications.
Keywords Diabetic Retinopathy (DR), Fundus Image, convolutional neural networks, Automated Disease Classification etc.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-28
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5768
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