Deep multi-view feature fusion with data augmentation for improved diabetic retinopathy classification

Yaakoub Boualleg, Kheir Eddine Daouadi, Oussama Guehairia, Chawki Djeddi, Abbas Cheddad, Imran Siddiqi, Bouderah Brahim

Abstract: Diabetic retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection to prevent severe visual impairment. Despite numerous proposed classification techniques, challenges persist due to the high parameter count of deep learning algorithms, imbalanced datasets, and limited performance. This study introduces a novel framework for DR classification that leverages multi-view deep features, multilinear whitened principal component analysis, tensor exponential discriminant analysis, synthetic minority oversampling technique, and deep random forest. We evaluated this architecture using the APTOS blindness dataset under a standard protocol. The results demonstrate that our architecture significantly improves classification accuracy, surpassing existing methods. Our contributions highlight a promising approach for enhancing DR classification performance.