Authors : Suyash Shinde ; Vikram Yadav ; Pranav Pawar ; Soham Kolte ; Om Shingare ; Sachin Jagadale

Volume/Issue : Volume 10 - 2025, Issue 5 - May

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Google Scholar : https://tinyurl.com/3yrevnkj

DOI : https://doi.org/10.38124/ijisrt/25may406

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Abstract : This paper suggests an automated technique for detecting diabetic retinopathy (DR), a major cause of visual loss. Deep learning algorithms and powerful image processing techniques are used to improve the accuracy of DR categorisation. The technique employs convolutional neural networks trained on labelled fundus images, which leads to considerable gains in classification metrics over existing methods. In terms of accuracy, precision, recall, F1-score, and AUC-ROC measures, the system performs better than current approaches. Clinical validation is aided by explainable AI features that offer visual insights into predictions. This method may lessen vision loss brought on by diabetes by providing a scalable option for early DR identification.

Keywords : Diabetic Retinopathy (DR), Vision Impairment, Early Detection, Automated Detection System, Fundus Images, Classification Accuracy, AI-Driven Approaches, Clinical Applications.

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