Lightweight Adaptive Grading Model for Diabetic Retinopathy Based on Ultra-Widefield Fundus Images - Miao, Xinpeng; Feng, Shuaichao; Zhao, Jie; Zhuo, Guangping; Ma, Fei (International Journal of Computer Science and Information Technology, 2025)
Wide-field fundus images play a crucial role in the diagnosis of diabetic retinopathy. However, traditional convolutional neural networks face limitations when processing such images, struggling to balance local details and global structures. To address this, a novel lightweight feature extraction network, called the Adaptive Nonlinear Lightweight Retina Network (ANLR-Net), is proposed. The network consists of two key modules: the Adaptive Feature Mapping Module (AFMM) and the Lightweight Module (LEM). AFMM replaces traditional linear weight matrices with learnable nonlinear functions, enabling precise capture of both global structures and local details while reducing network parameters and improving learning efficiency. LEM performs dimensionality reduction and restoration using 1×1 convolutions, which reduces computational cost while maintaining the network's lightweight nature. ANLR-Net effectively captures multi-scale features in wide-field images, significantly reducing computational complexity and parameter count. Experimental results show that ANLR-Net achieves a classification accuracy of 95.05%, specificity of 98.64%, and an AUC of 0.9712 on the wide-field dataset, outperforming traditional models.