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.