Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT
Aiming at the problems of inaccurate segmentation and low detection efficiency caused by irregular tumor shape and large size differences in brain MRI images, this study proposes a brain tumor segmentation algorithm, YOLO-BT, based on YOLOv11. YOLO-BT uses UNetV2 as the backbone network to enhance t...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/12/3645 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849704790479601664 |
|---|---|
| author | Mengying Xiong Aiping Wu Yue Yang Qingqing Fu |
| author_facet | Mengying Xiong Aiping Wu Yue Yang Qingqing Fu |
| author_sort | Mengying Xiong |
| collection | DOAJ |
| description | Aiming at the problems of inaccurate segmentation and low detection efficiency caused by irregular tumor shape and large size differences in brain MRI images, this study proposes a brain tumor segmentation algorithm, YOLO-BT, based on YOLOv11. YOLO-BT uses UNetV2 as the backbone network to enhance the feature extraction ability of key regions through the attention mechanism. The BiFPN structure is introduced into the neck network to replace the traditional feature splicing method, realize the two-way fusion of cross-scale features, improve detection accuracy, and reduce the amount of calculations required. The D-LKA mechanism is introduced into the C3k2 structure, and the large convolution kernel is used to process complex image information to enhance the model’s ability to characterize different scales and irregular tumors. In this study, multiple sets of experiments were performed on the Figshare Brain Tumor dataset to test the performance of YOLO-BT. The data results show that YOLO-BT improves Precision by 2.7%, Recall, mAP50 by 0.9%, and mAP50-95 by 0.3% in the candidate box-based evaluation compared to YOLOv11. In mask-based evaluations, Precision improved by 2.5%, Recall by 2.8%, mAP50 by 1.1%, and mAP50-95 by 0.5%. At the same time, the mIOU increased by 6.1%, and the Dice coefficient increased by 3.6%. It can be seen that the YOLO-BT algorithm is suitable for brain tumor detection and segmentation. |
| format | Article |
| id | doaj-art-fbaa19f651a54d55a8c08105f76df695 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-fbaa19f651a54d55a8c08105f76df6952025-08-20T03:16:39ZengMDPI AGSensors1424-82202025-06-012512364510.3390/s25123645Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BTMengying Xiong0Aiping Wu1Yue Yang2Qingqing Fu3School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, ChinaSchool of Computing Science and Artificial Intelligence, Suzhou City University, Suzhou 215104, ChinaHuizhou Customs Port Clinic, Huizhou International Travel Health Care Center, Huizhou Customs Comprehensive Technical Center, Huizhou 516006, ChinaSchool of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, ChinaAiming at the problems of inaccurate segmentation and low detection efficiency caused by irregular tumor shape and large size differences in brain MRI images, this study proposes a brain tumor segmentation algorithm, YOLO-BT, based on YOLOv11. YOLO-BT uses UNetV2 as the backbone network to enhance the feature extraction ability of key regions through the attention mechanism. The BiFPN structure is introduced into the neck network to replace the traditional feature splicing method, realize the two-way fusion of cross-scale features, improve detection accuracy, and reduce the amount of calculations required. The D-LKA mechanism is introduced into the C3k2 structure, and the large convolution kernel is used to process complex image information to enhance the model’s ability to characterize different scales and irregular tumors. In this study, multiple sets of experiments were performed on the Figshare Brain Tumor dataset to test the performance of YOLO-BT. The data results show that YOLO-BT improves Precision by 2.7%, Recall, mAP50 by 0.9%, and mAP50-95 by 0.3% in the candidate box-based evaluation compared to YOLOv11. In mask-based evaluations, Precision improved by 2.5%, Recall by 2.8%, mAP50 by 1.1%, and mAP50-95 by 0.5%. At the same time, the mIOU increased by 6.1%, and the Dice coefficient increased by 3.6%. It can be seen that the YOLO-BT algorithm is suitable for brain tumor detection and segmentation.https://www.mdpi.com/1424-8220/25/12/3645brain tumorcomputer visiondeep learningimage processingYOLOv11YOLO-BT |
| spellingShingle | Mengying Xiong Aiping Wu Yue Yang Qingqing Fu Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT Sensors brain tumor computer vision deep learning image processing YOLOv11 YOLO-BT |
| title | Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT |
| title_full | Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT |
| title_fullStr | Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT |
| title_full_unstemmed | Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT |
| title_short | Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT |
| title_sort | efficient brain tumor segmentation for mri images using yolo bt |
| topic | brain tumor computer vision deep learning image processing YOLOv11 YOLO-BT |
| url | https://www.mdpi.com/1424-8220/25/12/3645 |
| work_keys_str_mv | AT mengyingxiong efficientbraintumorsegmentationformriimagesusingyolobt AT aipingwu efficientbraintumorsegmentationformriimagesusingyolobt AT yueyang efficientbraintumorsegmentationformriimagesusingyolobt AT qingqingfu efficientbraintumorsegmentationformriimagesusingyolobt |