International Journal of Embedded Systems and Emerging Technologies
Diabetic Retinopathy Detection from Retinal Images Using Convolutional Neural Network
Abstract
Diabetic retinopathy (DR) is a popular problem of diabetes and the major reasons of loss of sight in the active inhabitants. Many of the problems of DR can be avoided by blood glucose manage and regular remedy. Since the kinds and the difficulties of DR, it is actually hard for DR detection in the time-consuming regular analysis. This document is to effort towards discovering an automated method to sort out a provided set of fundus images. We offer convolutional neural networks (CNNs) power to DR detection, which entails classification, segmentation, and detection as its three main difficult problems. We track AlexNet, VggNet, GoogleNet, and ResNet and examine how well these types perform in conjunction with DR image categorization using transfer learning and hyper-parameter tuning. We utilize publicly available Kaggle platform for training these versions. The greatest classification accuracy is 95.68% and the outcomes have confirmed the better accuracy of CNNs and transfer learning on DR image classification.References (24)
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