Binary Classification of Diabetic Retinopathy Detection and Web Application
Keywords:Contrast Limited Adaptive Histogram Equalization (CLAHE), Convolutional Neural Network (CNN), Deep Learning, Diabetic Retinopathy (DR), Gaussian-blur filter
Now-a-days with increasing cases of diabetes one should control the blood sugar as well as should perform regular examination of eye, which can prevent the person from blindness. Any person having diabetes can develop Diabetic Retinopathy (DR). DR is triggered by high blood sugar due to diabetes. After some time having excessive amount of sugar in blood can damage retina. When sugar jams the tiny blood vessels the eyes are damaged affecting the blood vessels in leakage of fluid. Millions of working aged adults suffer from loss of sight due to diabetic retinopathy. DR cannot be treated completely but early detection of DR prevents the person from vision loss. We proposed a deep learning model for detection of Diabetic Retinopathy. Detection of DR is a slow process. Physical detection of DR involves a trained clinician to study and estimate the color fundus photographs of the retina. Normal process of identification takes minimum two days. In our project we used Convolutional Neural Network architecture is used to classify images into two classes which is no-diabetic retinopathy and with diabetic retinopathy. APTOS-2019 Blindness Detection dataset is used from Kaggle which contains high resolution Retinal images. Those images are used to train the model. Web based interface is created for easy interaction with the model.
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Copyright (c) 2021 P. M. Vaibhavi, R. Manjesh, Sushmitha
This work is licensed under a Creative Commons Attribution 4.0 International License.