MSAmix-Net: Diabetic Retinopathy Classification - Jianyun Gao; Shu Li; Yiwen Chen; Rongwu Xiang (IEEE Access)
Diabetic retinopathy (DR) is a very common complication of diabetes that can lead to retinal damage, affecting vision. If not detected and diagnosed in time, it may result in visual impairment. With the development of deep learning, various automatic diagnosis models for DR have been proposed. Most models are based on convolutional neural networks, but due to the small size of convolution kernels in shallow networks, the receptive field is limited, preventing the capture of global information. This limitation forces models to require more convolutional layers to expand the receptive field and capture global information related to DR. In this paper, we use a combined module of multiscale convolution and convolutional self-attention, and we adjust the module structure, allowing the model to have both long-range dependency modeling capability and CNN’s multiscale local feature extraction capability, while having fewer model parameters than the selected comparison models. Our proposed MSAmix-Net achieved an accuracy of 82.3% in the five-class classification task on the combined dataset of APTOS-2019 and Messidor-2, and an accuracy of 93.9% in the three-class classification task on the DRAC-2022 dataset. The experiments demonstrate that the model can effectively capture and represent the complex features of diabetic retinopathy.