Open Access

ARTICLE

CMS-YOLO: An Automated Multi-Category Brain Tumor Detection Algorithm Based on Improved YOLOv10s

Li Li, Xiao Wang^*, Ran Ding, Linlin Luo, Qinmu Wu, Zhiqin He

Department of Electrical Engineering, Guizhou University, Guiyang, 550025, China

* Corresponding Author: Xiao Wang. Email:

Computers, Materials & Continua 2025, 85(1), 1287-1309. https://doi.org/10.32604/cmc.2025.065670

Received 19 March 2025; Accepted 24 June 2025; Issue published 29 August 2025

Abstract

Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues, and their appearance may lead to a series of complex symptoms. However, current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology, size, and complex background, resulting in low detection accuracy, high rate of misdiagnosis and underdiagnosis, and challenges in meeting clinical needs. Therefore, this paper proposes the CMS-YOLO network model for multi-category brain tumor detection, which is based on the You Only Look Once version 10 (YOLOv10s) algorithm. This model innovatively integrates the Convolutional Medical UNet extended block (CMUNeXt Block) to design a brand-new CSP Bottleneck with 2 convolutions (C2f) structure, which significantly enhances the ability to extract features of the lesion area. Meanwhile, to address the challenge of complex backgrounds in brain tumor detection, a Multi-Scale Attention Aggregation (MSAA) module is introduced. The module integrates features of lesions at different scales, enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios. Finally, during the model training process, the Shape-IoU loss function is employed to replace the Complete-IoU (CIoU) loss function for optimizing bounding box regression. This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours, thereby further enhancing the detection precision. The experimental results show that the improved method achieves 94.80% precision, 93.60% recall, 96.20% score, and 79.60% on the MRI for Brain Tumor with Bounding Boxes dataset. Compared to the YOLOv10s model, this represents improvements of 1.0%, 1.1%, 1.0%, and 1.1%, respectively. The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma, meningioma, and pituitary tumor, which can accurately detect and identify brain tumors, assist doctors in early diagnosis, and promote the development of early treatment.

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