Automatic Severity Classification of Diabetic Retinopathy Based on DenseNet and Convolutional Block Attention Module

Abstract

Diabetic Retinopathy (DR) - a complication developed due to heightened blood glucose levels- is deemed one of the most sight-threatening diseases. Unfortunately, DR screening is manually acquired by an ophthalmologist, a process that can be considered erroneous and time-consuming. Accordingly, automated DR diagnostics have become a focus of research in recent years due to the tremendous increase in diabetic patients. Moreover, the recent accomplishments demonstrated by Convolutional Neural Networks (CNN) settle them as state-of-the-art for DR stage identification. This paper proposes a new automatic deep-learning-based approach for severity detection by utilizing a single Color Fundus photograph (CFP). The proposed technique employs DenseNet169's encoder to construct a visual embedding. Furthermore, Convolutional Block Attention Module (CBAM) is introduced on top of the encoder to reinforce its discriminative power. Finally, the model is trained using cross-entropy loss on the Kaggle Asia Pacific Tele-Ophthalmology Society's (APTOS) dataset. On the binary classification task, we accomplished (97% accuracy - 97% sensitivity - 98.3% specificity - 0.9455, Quadratic Weighted Kappa score (QWK)) compared to the state-of-the-art. Moreover, Our network showed high competency (82% accuracy - 0.888 (QWK)) for severity grading. The significant contribution of the proposed framework is that it efficiently grades the severity level of diabetic retinopathy while reducing the time and space complexity required, which demonstrates it as a promising candidate for autonomous diagnosis.