@article{Wang2025, 
author = {Honglin Wang and Yaohua Xu and Cheng Zhu},
title = {Enhancing Medical Image Classification with BSDA-Mamba: Integrating Bayesian Random Semantic Data Augmentation and Residual Connections},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {83},
number = {3},
pages = {4999-5018},
keywords = {Deep learning, medical image classification, data augmentation, visual state space model},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.061848},
doi = {10.32604/cmc.2025.061848},
abstract = {Medical image classification is crucial in disease diagnosis, treatment planning, and clinical decision-making. We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation (BSDA) with a Vision Mamba-based model for medical image classification (MedMamba), enhanced by residual connection blocks, we named the model BSDA-Mamba. BSDA augments medical image data semantically, enhancing the model’s generalization ability and classification performance. MedMamba, a deep learning-based state space model, excels in capturing long-range dependencies in medical images. By incorporating residual connections, BSDA-Mamba further improves feature extraction capabilities. Through comprehensive experiments on eight medical image datasets, we demonstrate that BSDA-Mamba outperforms existing models in accuracy, area under the curve, and F1-score. Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis, particularly in handling diverse imaging modalities from X-rays to MRI. The open-sourcing of our model’s code and datasets, will facilitate the reproduction and extension of our work.}
}