An efficient explainable deep neural network classifier for diabetic retinopathy detection

Abstract

Early detection of Diabetic Retinopathy (DR) is crucial for effective intervention, particularly given its often asymptomatic nature in initial stages. Automated detection systems offer significant advantages, such as improving screening efficiency, extending healthcare accessibility to remote regions, and facilitating proactive disease management. Introducing an innovative framework for retinal image analysis that combines explainable deep learning, hybrid feature extraction, and advanced data augmentation to optimize performance and interpretability for clinical applications. The xDNN model, trained on the MESSIDOR-2 dataset, achieved an average recall of 98%, precision of 98.2%, F1-score of 98%, and accuracy of 98.2%. When extensively trained on the APTOS 2019 dataset, the model delivered outstanding results with an average precision, recall, and F1-score of 99%, and an accuracy of 99.7%. Additionally, the model's performance on the IDRID dataset was remarkable, with average precision, recall, F1-score, and accuracy all reaching 99%. Noteworthy is our method impressive average Area Under the Curve (AUC) of 99.8%, affirming its consistent and exceptional performance across all classes of diabetic retinopathy. This underscores the xDNN Classifier's potential as a valuable tool for precise and reliable diabetic retinopathy detection. It holds substantial promise for elevating clinical diagnosis and enhancing treatment outcomes within the field of ophthalmology.