SDCSCF ‐Net: A High‐Performance Spatial Channel Fusion Attention Network for Diabetic Retinopathy Classification

ABSTRACT Diabetic retinopathy (DR) is a leading cause of blindness among individuals with diabetes. Timely diagnosis and precise classification of DR are essential for patients. However, the traditional diagnostic methods have limitations in precision, mainly relying on doctors' experiences and subjective judgments on DR images. Therefore, an efficient network model, named SDCSCF‐Net, is proposed based on deep learning for DR diagnosis and classification. Firstly, the SE_Double_Conv (SDC) module is designed by integrating the Squeeze‐and‐Excitation (SE) attention mechanism into the first Double_Conv block of the encoder structure of U‐Net to enhance feature representation and suppress redundant information. Secondly, a novel attention mechanism, spatial channel fusion attention (SCFA) module, is proposed to enhance the model's focus on lesion areas and the relationship between the channels, making the model more effectively distinguish subtle differences between adjacent DR classes. Finally, the proposed model is evaluated on the APTOS 2019 dataset, which contains 3662 fundus images. The results show that the proposed model demonstrates superior classification performance for DR compared to other existing approaches, and its accuracy, precision, recall, and F1‐score for binary classification of DR are 99.18%, 99.47%, 98.98%, and 99.19%, respectively. For the five‐class classification task, the model achieves an accuracy of 84.72%, a precision of 84.12%, a recall of 84.72%, and an F1‐score of 84.02%. All the evaluation metrics are obtained from the testing phase of the model. In addition, the Grad‐CAM technology is utilized to visualize the key lesion areas concerned by the model and further verifies the effectiveness of the proposed model. It is beneficial to promote the research and practical application in the intelligent diagnosis of DR.