A Novel Dual Attention Approach for DNN Based Automated Diabetic Retinopathy Grading

Abstract

Diabetic retinopathy (DR) poses a serious threat to vision, emphasising the need for early detection. Manual analysis of fundus images, though common, is error-prone and time-intensive. Existing automated diagnostic methods lack precision, particularly in the early stages of DR. This paper introduces the Soft Convolutional Block Attention Module-based Network (Soft-CBAMNet), a deep learning network designed for severity detection, which features Soft-CBAM attention to capture complex features from fundus images. The proposed network integrates both the convolutional block attention module (CBAM) and the soft-attention components, ensuring simultaneous processing of input features. Following this, attention maps undergo a max-pooling operation, and refined features are concatenated before passing through a dropout layer with a dropout rate of 50%. Experimental results on the APTOS dataset demonstrate the superior performance of Soft-CBAMNet, achieving an accuracy of 85.4% in multiclass DR grading. The proposed architecture has shown strong robustness and general feature learning capability, achieving a mean AUC of 0.81 on the IDRID dataset. Soft-CBAMNet's dynamic feature extraction capability across all classes is further justified by the inspection of intermediate feature maps. The model excels in identifying all stages of DR with increased precision, surpassing contemporary approaches. Soft-CBAMNet presents a significant advancement in DR diagnosis, offering improved accuracy and efficiency for timely intervention.