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Pneumonia is a disease characterized by high mortality, and research on its treatment and early screening tools has garnered significant attention. However, the complexity of chest images, annotation difficulty, and the inter-class similarities and intra-class variations of pneumonia all pose challenges to pneumonia classification and localization tasks based on X-ray images. Firstly, a self-supervised learning approach utilizing a siamese network was applied to extract self-supervised visual feature representation of chest X-ray images, and a multi-layer perceptron(MLP) detection head was used to perform pneumonia classification task. Secondly, a spatial-channel attention module was designed in the backbone network of YOLOv10. By utilizing the spatial information across channels to enhance pneumonia features, the ability of the YOLOv10 network to identify pneumonia lesions was enhanced. Finally, the proposed algorithm was evaluated using the COVID-19 Radiography Database and RSNA Pneumonia Detection dataset. The results demonstrated its effectiveness of the proposed algorithm in pneumonia classification and localization tasks based on X-ray images.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2025016
China Classification Code:R563.1;TP18;TP391.41
Citation Information:
[1]WEN Yaxue,LI Yuqin,JIANG Zhengang ,et al.Research on Pneumonia Classification and Localization Methods Based on X-ray Imaging[J].Journal of Zhengzhou University(Natural Science Edition)().DOI:10.13705/j.issn.1671-6841.2025016.
Fund Information:
吉林省教育厅科学技术研究项目(JJKH20240948KJ)
2026-04-24
2026-04-24
2026-04-24