DiabNet: A Convolutional Neural Network for Diabetic Retinopathy Detection
E evaluation on a standard dataset shows that DiabNet surpasses existing methods in accuracy, efficiency, and robustness, and it is noted that DiabNet, as a mobile app, has the potential to significantly impact the field of diabetic retinopathy detection, leading to improved early detection of diabetic retinopathy.
Abstract
Diabetic retinopathy is a leading cause of blindness among diabetic patients, and early detection is crucial. This research proposes DiabNet, a novel convolutional neural network (CNN) architecture designed to enhance the accuracy, efficiency, and robustness of diabetic retinopathy detection from retinal images. DiabNet incorporates unique features like skip connections, attention mechanisms, and batch normalisation to improve feature extraction. The paper details DiabNet’s architecture, feature extraction, and training process. Evaluation on a standard dataset shows that DiabNet surpasses existing methods in accuracy, efficiency, and robustness. The research also explores the interpretability of DiabNet and suggests future research directions. The potential impact of DiabNet includes improved early detection and management of diabetic retinopathy. In addition, DiabNet’s deployment as a mobile app enables convenient and accessible diabetic retinopathy screening. Finally, it is noted that DiabNet, as a mobile app, has the potential to significantly impact the field of diabetic retinopathy detection, leading to improved early detection of diabetic retinopathy. The experimental validation proves that the proposed DiabNet architecture is feasible for real-time deployment yielding an accuracy of 98.72%.