Efficient fusion transformer model for accurate classification of eye diseases.

The automatic diagnosis model of medical image based on deep learning can improve the diagnosis efficiency and reduce the diagnosis cost. At present, there is a lack of research on special artificial intelligence models for medical image analysis of fundus disease characteristics. Considering that fundus diseases have both local and global features, this paper proposes a novel deep learning model Local-Global Scale Fusion Network (LGSF-Net). The novelty lies in a dual-stream fusion design that processes global context (Transformer) and local details (CNN) in parallel with residual fusion. On the public fundus dataset, LGSF-Net delivers 96% accuracy with only 18.7K parameters and 0.93 GFLOPs, outperforming existing state-of-the-art universal methods like ResNet50 and ViT. LGSF-Net is more suitable for clinical diagnosis because of its accuracy and lightweight design. The ablation study shows that the concept of LGSF-Net multi-scale fusion understanding has been correctly realized. This work effectively promotes the development of smart medicine and provides a new solution for the design of new deep learning models.