ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1597378
CoroYOLO: A Novel Colorectal Cancer Detection Method Based on the Mamba Framework
Provisionally accepted- Beijing University of Chemical Technology, Beijing, China
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Colorectal cancer (CRC) is one of the most common malignant tumors worldwide, and early detection is crucial for improving cure rates. In recent years, object detection methods based on convolutional neural networks (CNNs) and transformers have made significant progress in medical image analysis. However, CNNs have limitations in capturing global contextual information, and while transformers can handle long-range dependencies, their high computational complexity limits their efficiency in practical applications. To address these issues, this paper proposes a novel object detection model-CoroYOLO. CoroYOLO builds upon the YOLOv10 architecture by incorporating the concept of State Space Model (SSM) and introduces the TSMamblock module, which dynamically models the input data, reduces redundant computations, and improves both computational efficiency and detection accuracy. Additionally, CoroYOLO integrates the Efficient Multi-Scale Attention (EMA) mechanism, which adaptively strengthens focus on critical regions, enhancing the model's robustness in complex medical images. Experimental results show that after training on the SUN Polyp and PICCOLO datasets, CoroYOLO outperforms existing mainstream methods on the Etis-Larib dataset, achieving state-of-the-art performance and demonstrating the model's effectiveness for early colorectal cancer detection.
Keywords: Colorectal cancer detection, State space model, deep learning, Mamba, YOLOv10
Received: 21 Mar 2025; Accepted: 30 Apr 2025.
Copyright: © 2025 Chen, Hou and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wenfei Chen, Beijing University of Chemical Technology, Beijing, China
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