CLASSIFICATION OF RETINAL DISEASES USING RESNET MODEL

Registration ID: IJNRD_197084

Published ID: IJNRD2305761

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Arde Apeksha Balasaheb , Deshmukh Sonal Kiran , Jagtap Om Chandrakant , Patekar Onkar Nitin

Diabetic Retinopathy, ResNet Model, Feature extraction, Image pro- cessing quality, classification.

Retinopathy is the most prevalent cause of avoidable vision impairment, mostly affecting the working-age population in the world. Recent research has given a better understanding of the clinical eye care practice to identify better and cheaper ways of identifying, understanding, managing, diagnosing, and treating Retinal Diseases. The number of people suffering from Diabetic Retinopathy is much more when compared to that of the ophthalmologists present. So a computeraided diagnosis tool is required which detects and classifies the fundus retinal image. In previous studies, the deep learning systems were usually trained directly end-to-end from original fundus images to the labels of DR grades, these end-toend systems might fail to encode the lesion features due to the Black-box nature of deep learning. In our study, we improved the image processing quality using the RESNET model and increased the accuracy above 90 percent. The GUI we developed classified the images into different classes of DR. These classes include No DR, Mild DR, Moderate DR, and Proliferate DR.

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Arde Apeksha Balasaheb, Deshmukh Sonal Kiran, Jagtap Om Chandrakant, & Patekar Onkar Nitin (May-2023). CLASSIFICATION OF RETINAL DISEASES USING RESNET MODEL. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), h455-h459. https://ijnrd.org/papers/IJNRD2305761.pdf

Volume 8 Issue 5, May-2023

Pages : h455-h459

Paper Reg. ID: IJNRD_197084

Published Paper Id: IJNRD2305761

Downloads: 000121980

Research Area: Engineering

Country: Ahmednagar, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305761.pdf

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