DFCAFNet: Dual-feature co-attentive fusion network for diabetic retinopathy grading

Abstract

Diabetic retinopathy (DR) grading is a complicated task characterized by inter-class variations among several categories, the subtle detection of tiny lesions, and uneven data distribution. To achieve precise DR grading, extracting distinct features that can effectively capture minor visual variations in lesions is necessary. The constraints of conventional convolutional neural networks (CNNs) are evident in their difficulty in detecting tiny lesions, which is further enhanced by their biased focus due to an uneven distribution of data. Furthermore, comparable color and texture attributes among different classes add to the complexity of achieving accurate grading results. This paper presents a new multi-scale attention fusion network (MS-AFNet) incorporating multi-scale contextual information. By successfully combining local and global features, the network improves the diagnostic accuracy of the model in handling inter-class variances. A concurrent attention module (CAM) with multiple attention blocks enhances the ability to distinguish information by capturing complex features within the lesions through numerous attention mechanisms. This novel approach greatly improves the model’s capacity to distinguish nuanced features in minor lesions, resulting in more precise diagnostic outcomes. A dual-feature co-attentive fusion network (DFCAFNet) is proposed by combining CAM and MS-AFNet, which capture spatial patterns and highlight inter-channel interactions to detect complex patterns and enhance accuracy. An extensive assessment of two datasets on DR demonstrates the exceptional accuracy and computational efficiency of DFCAFNet.