Med-Former: A Transformer Based Architecture for Medical Image Classification

In recent years, transformer-based image classification methods have demonstrated remarkable effectiveness across various image classification tasks. However, their application to medical images presents challenges, especially in the feature extraction capability of the network. Additionally, these models often struggle with the efficient propagation of essential information throughout the network, hindering their performance in medical imaging tasks. To overcome these challenges, we introduce a novel framework comprising Local-Global Transformer module and Spatial Attention Fusion module, collectively referred to as Med-Former. These modules are specifically designed to enhance the feature extraction capability at both local and global levels and improve the propagation of vital information within the network. To evaluate the efficacy of our proposed Med-Former framework, we conducted experiments on three publicly available medical image datasets: NIH Chest X-ray14, DermaMNIST, and BloodMNIST. Our results demonstrate that Med-Former outperforms state-of-the-art approaches underscoring its superior generalization capability and effectiveness in medical image classification.