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- Title
Prediction of Diabetes through Retinal Images Using Deep Neural Network.
- Authors
Ragab, Mahmoud; AL-Ghamdi, Abdullah S. AL-Malaise; Fakieh, Bahjat; Choudhry, Hani; Mansour, Romany F.; Koundal, Deepika
- Abstract
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.
- Subjects
RETINAL imaging; RETINA; CONVOLUTIONAL neural networks; DIABETIC retinopathy; MACULAR edema; DEEP learning
- Publication
Computational Intelligence & Neuroscience, 2022, p1
- ISSN
1687-5265
- Publication type
Article
- DOI
10.1155/2022/7887908