Diabetic retinopathy grading based on multi-scale residual network and cross-attention module

Abstract

Diabetic retinopathy (DR) is a severe effect of diabetes mellitus that mainly impacts the retinal tissue and carries a high risk of blindness. Ophthalmologists face challenges in assessing the severity of DR due to its complexity and time constraints. Consequently, there is an urgent need for the development of automated methods that employ retinal fundus images to detect DR. This study introduces a novel deep learning architecture that utilizes a multi-scale residual attention block (MSRAB) and a cross-attention block (CrAB) for DR grading. The proposed MSRAB employs a convolutional neural network (CNN) with diverse dilation rates to expand its field of view. MSRAB adaptively concentrate on pertinent features and incorporates a residual attention network to improve grading performance. Integrating the residual attention network enables MSRAB to prioritize critical characteristics in retinal fundus images and enhance performance. Similarly, the CrAB integrates channel and spatial attention mechanisms to capture inter-channel interactions and spatial dependencies within the input features. This comprehensive methodology enables the model to concentrate more efficiently on critical regions to discriminate irrelevant features and to capture interrelations across different channels and spatial regions. The proposed model employs a pre-trained backbone network to extract local and global features to capture complex features for precisely identifying DR. This process enhances model efficiency, which is helpful in methods with limited data and processing limitations. The suggested methodology surpasses the existing methodologies in accuracy, recall, precision, and Area Under the Curve.