Information Systems for Convolutional Neural Network in Diabetes Detection Using Fundus Images - Razzaq , Wafaa (Academia Open, 2025)
Diabetes is regarded as a serious illness, and early detection enhances patient treatment and quality of life. This research develops a convolutional neural network for diabetes detection using fundus images from the IDRID image set. The convolutional neural network's performance analysis is applied to cross-validation and comparison with other patterns found in earlier research using techniques including Random Forest, Extra Trees, SVM, and AdaBoost. Our CNN pattern consists of an input layer, three convolutional layers, three pooling layers, two fully connected layers, and an output layer with two neurons. An accuracy of 83.50% was obtained from the evaluation of the convolutional neural network pattern using 103 photos, which is better than the results of earlier research. Highlights: CNN developed for diabetes detection using IDRiD fundus images. Compared with RF, ET, SVM, and AdaBoost via cross-validation. CNN achieved 83.50% accuracy, outperforming previous research. Keywords: Convolutional Neural Network, Diabetes Screening, Machine Learning, Deep Learning.