• Volume: 1
• Issue:01
• Date: 01-01-2024

Title : Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning- A Review

Abstract: This review paper explores the Diabetic Retinopathy (DR) is a severe complication of diabetes that affects the retina and can lead to vision loss if not detected and treated early. an analysis of Diabetic Retinopathy based on deep learning approaches. Convolutional Neural Networks (CNNs) and other deep learning architectures are employed to automatically extract relevant features from retinal images, enabling efficient and accurate detection of DR stages. The model is trained on large datasets of annotated retinal images to learn intricate patterns associated with different DR severity levels. The review explores the interpretability of the deep learning models, aiming to enhance the trust and acceptance of these automated systems in clinical settings. Interpretability methods such as attention maps and saliency maps are employed to provide insights into the decision-making process of the model, aiding in understanding the features that contribute to the classification of DR.

Key Words: Deep Learning, Classification, Diabetic Retinopathy, DR

Area: Engineering

• Approved ISSN: ----
• Paper Id: IJREISTU3
• Page No: 15-19

• Author: Gagan Deep Kaur

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• Co- Author: Prof. Pooja Meena