MBSM-Net: A Multi-Branch Structure Model for Pneumoconiosis Screening and Grading of Chest X-Ray Images

Abstract

Convolutional neural network (CNN)-based auxiliary diagnostic systems have been widely proposed. However, CNNs have limitations in perceiving global features and more subtle features, which makes existing methods unable to achieve ideal accuracy in tasks such as pneumoconiosis screening. To overcome these limitations, we propose MBSM-Net, a new multi-branch structure-enhanced model for pneumoconiosis screening and grading based on X-ray images. MBSM-Net introduces an adaptive feature selection and fusion module to achieve synchronous extraction and hierarchical fusion of global and local features. In the local feature extraction module, we designed a CNN-Mamba module. This module integrates prior information through a detailed enhancement module to compensate for the shortcomings of traditional convolutions and significantly enhances the expression of subtle lesion information. Meanwhile, the Mamba module simulates pixel-level long-range dependencies to extract finer-grained texture features. In the global feature extraction module, we cleverly utilize the windowed multi-head self-attention (W-MSA) mechanism, enabling the model to better understand the overall distribution and degree of fibrosis of pulmonary lesions. We validated the MBSM-Net model on 1,760 real anonymized patient X-ray chest films. The results showed that the accuracy of the MBSM-Net model reached 78.6%, and the F1 score reached 79%, both of which are superior to existing models.