YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance
Brain tumors are high-risk conditions where early detection and precise localization are crucial for improving patient prognosis. However, existing automated detection methods still exhibit limitations in robustness within complex backgrounds, boundary recognition, and the detection of small tumors,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825000894 |
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| _version_ | 1832087713770110976 |
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| author | Jian Huang Wen Ding Tiancheng Zhong Gang Yu |
| author_facet | Jian Huang Wen Ding Tiancheng Zhong Gang Yu |
| author_sort | Jian Huang |
| collection | DOAJ |
| description | Brain tumors are high-risk conditions where early detection and precise localization are crucial for improving patient prognosis. However, existing automated detection methods still exhibit limitations in robustness within complex backgrounds, boundary recognition, and the detection of small tumors, making it challenging to meet the high precision requirements of clinical applications. To address these issues, this paper proposes an improved YOLOv10-based model, YOLO-TumorNet. Specifically, YOLO-TumorNet integrates the InceptionNeXt architecture, Multi-Scale Spatial Pyramid Attention (MSPA), and Bidirectional Feature Pyramid Network (BiFPN) modules to enhance multi-scale feature extraction and channel attention mechanisms, thereby improving the model’s accuracy and robustness in brain tumor detection. Additionally, extensive experiments conducted on the Br35H and Roboflow datasets demonstrate the superior performance of YOLO-TumorNet in terms of boundary clarity, detail capture, and small tumor detection. |
| format | Article |
| id | doaj-art-483911d83bf2492caea6b88ef95f33c3 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-483911d83bf2492caea6b88ef95f33c32025-02-06T05:11:11ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119211221YOLO-TumorNet: An innovative model for enhancing brain tumor detection performanceJian Huang0Wen Ding1Tiancheng Zhong2Gang Yu3National Clinical Research Center for Child Health, National Children’s Regional Medical Center, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China; Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou 310052, ChinaNational Clinical Research Center for Child Health, National Children’s Regional Medical Center, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, ChinaShenzhen Jancsitech Co., Limited, Shenzhen, 518100, ChinaNational Clinical Research Center for Child Health, National Children’s Regional Medical Center, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China; Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou 310052, China; Corresponding author at: National Clinical Research Center for Child Health, National Children’s Regional Medical Center, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China.Brain tumors are high-risk conditions where early detection and precise localization are crucial for improving patient prognosis. However, existing automated detection methods still exhibit limitations in robustness within complex backgrounds, boundary recognition, and the detection of small tumors, making it challenging to meet the high precision requirements of clinical applications. To address these issues, this paper proposes an improved YOLOv10-based model, YOLO-TumorNet. Specifically, YOLO-TumorNet integrates the InceptionNeXt architecture, Multi-Scale Spatial Pyramid Attention (MSPA), and Bidirectional Feature Pyramid Network (BiFPN) modules to enhance multi-scale feature extraction and channel attention mechanisms, thereby improving the model’s accuracy and robustness in brain tumor detection. Additionally, extensive experiments conducted on the Br35H and Roboflow datasets demonstrate the superior performance of YOLO-TumorNet in terms of boundary clarity, detail capture, and small tumor detection.http://www.sciencedirect.com/science/article/pii/S1110016825000894IoT in healthcareYOLO-TumorNetBrain tumor detectionMedical imagingMulti-scale feature extraction |
| spellingShingle | Jian Huang Wen Ding Tiancheng Zhong Gang Yu YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance Alexandria Engineering Journal IoT in healthcare YOLO-TumorNet Brain tumor detection Medical imaging Multi-scale feature extraction |
| title | YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance |
| title_full | YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance |
| title_fullStr | YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance |
| title_full_unstemmed | YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance |
| title_short | YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance |
| title_sort | yolo tumornet an innovative model for enhancing brain tumor detection performance |
| topic | IoT in healthcare YOLO-TumorNet Brain tumor detection Medical imaging Multi-scale feature extraction |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825000894 |
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