文章基本信息

标题：Prediction of Diabetes through Retinal Images Using Deep Neural Network
本地全文：下载
作者：Mahmoud Ragab ; Abdullah S. AL-Malaise AL-Ghamdi ; Bahjat Fakieh 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2022
卷号：2022
DOI：10.1155/2022/7887908
语种：English
出版社：Hindawi Publishing Corporation
摘要：Microvascular problems of diabetes, such as diabetic retinopathy and macular edema, can be seen in the eye's retina, and the retinal images are being used to screen for and diagnose the illness manually. Using deep learning to automate this time-consuming process might be quite beneficial. In this paper, a deep neural network, i.e., convolutional neural network, has been proposed for predicting diabetes through retinal images. Before applying the deep neural network, the dataset is preprocessed and normalised for classification. Deep neural network is constructed by using 7 layers, 5 kernels, and ReLU activation function, and MaxPooling is implemented to combine important features. Finally, the model is implemented to classify whether the retinal image belongs to a diabetic or nondiabetic class. The parameters used for evaluating the model are accuracy, precision, recall, and F1 score. The implemented model has achieved a training accuracy of more than 95%, which is much better than the other states of the art algorithms.