Diabetic Retinopathy Detection Using Transfer Learning

Diabetic Retinopathy Detection Using Transfer Learning

R. Parvathi, U. Vignesh
Copyright: © 2023 |Pages: 28
DOI: 10.4018/979-8-3693-0876-9.ch011
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Abstract

The proposed research work deals with the image processing and machine learning algorithms to examine fundus images for the diagnosis and categorization of diabetic retinopathy, which affects a substantial number of humans worldwide due to diabetes. Pre-processing and feature extraction from the extracted images will be part of the research, along with training and assessing of deep learning models for categorization technique. The proposed model will predict the stage of diabetic retinopathy from fundus retinal images. The research carried out provides an output stages of diabetic conditions in a patient, such as: No Diabetic Retinopathy, Mild, Moderate, Severe, and Proliferative. The ultimate aim of the model is to develop a tool that will help medical practitioners diagnose using technologies such as artificial intelligence or internet of things, etc. for treating diabetic retinopathy to improve efficient outcomes on diagnosis.
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2. Literature Review

The paper (Bilal et al., 2021). by V. Deepa, C. S. Kumar and T. Cherian proposes a novel approach to automatically detect diabetic retinopathy (DR) using deep learning techniques. The proposed approach uses a convolutional neural network (CNN) to classify retinal images as normal or abnormal. This work consists of four pre-trained models for binary classification of fundus retina images. The proposed method uses deep learning techniques, which have shown great success in various computer vision tasks, including medical image analysis. The method achieves high accuracy and specificity, indicating its potential for use in clinical settings (Khang & Vladimir et al., 2023).

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