Open Access

ARTICLE

Enhancing Medical Image Classification with BSDA-Mamba: Integrating Bayesian Random Semantic Data Augmentation and Residual Connections

Honglin Wang¹, Yaohua Xu^2,*, Cheng Zhu³

1 School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China
3 Electrical & Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA

* Corresponding Author: Yaohua Xu. Email:

(This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)

Computers, Materials & Continua 2025, 83(3), 4999-5018. https://doi.org/10.32604/cmc.2025.061848

Received 04 December 2024; Accepted 07 March 2025; Issue published 19 May 2025

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.

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Keywords

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