Application of MRI image segmentation algorithm for brain tumors based on improved YOLO
ObjectiveTo assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades i...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1510175/full |
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| author | Tao Yang Xueqi Lu Lanlan Yang Miyang Yang Jinghui Chen Hongjia Zhao |
| author_facet | Tao Yang Xueqi Lu Lanlan Yang Miyang Yang Jinghui Chen Hongjia Zhao |
| author_sort | Tao Yang |
| collection | DOAJ |
| description | ObjectiveTo assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.MethodsThe research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images. From Dataset 1, we randomly selected 3,000 images and used the Labelimg tool to annotate the cancerous regions within the images. These images were then divided into training and validation sets in a 7:3 ratio. The remaining 223 images, along with Dataset 2, were ultimately used as the internal test set and external test set, respectively, to evaluate the model’s segmentation effect. A series of optimizations were made to the original YOLOv5 algorithm, introducing the Atrous Spatial Pyramid Pooling (ASPP), Convolutional Block Attention Module (CBAM), Coordinate Attention (CA) for structural improvement, resulting in several optimized versions, namely YOLOv5s-ASPP, YOLOv5s-CBAM, YOLOv5s-CA, YOLOv5s-ASPP-CBAM, and YOLOv5s-ASPP-CA. The training and validation sets were input into the original YOLOv5s model, five optimized models, and the YOLOv8s model for 100 rounds of iterative training. The best weight file of the model with the best evaluation index in the six trained models was used for the final test of the test set.ResultsAfter iterative training, the seven models can segment and recognize brain tumor magnetic resonance images. Their precision rates on the validation set are 92.5, 93.5, 91.2, 91.8, 89.6, 90.8, and 93.1%, respectively. The corresponding recall rates are 84, 85.3, 85.4, 84.7, 87.3, 85.4, and 91.9%. The best weight file of the model with the best evaluation index among the six trained models was tested on the test set, and the improved model significantly enhanced the image segmentation ability compared to the original model.ConclusionCompared with the original YOLOv5s model, among the five improved models, the improved YOLOv5s-ASPP model significantly enhanced the segmentation ability of brain tumor magnetic resonance images, which is helpful in assisting clinical diagnosis and treatment planning. |
| format | Article |
| id | doaj-art-cbec72f1edfc410ebaeaa9032a0e7406 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj-art-cbec72f1edfc410ebaeaa9032a0e74062025-01-07T06:48:04ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-01-011810.3389/fnins.2024.15101751510175Application of MRI image segmentation algorithm for brain tumors based on improved YOLOTao Yang0Xueqi Lu1Lanlan Yang2Miyang Yang3Jinghui Chen4Hongjia Zhao5The First Clinical Medical College, The Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, ChinaSchool of Biomedical Engineering, Southern Medical University, Guangzhou, ChinaThe First Clinical Medical College, The Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, ChinaThe First Clinical Medical College, The Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, ChinaThe First Clinical Medical College, The Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, ChinaThe Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaObjectiveTo assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.MethodsThe research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images. From Dataset 1, we randomly selected 3,000 images and used the Labelimg tool to annotate the cancerous regions within the images. These images were then divided into training and validation sets in a 7:3 ratio. The remaining 223 images, along with Dataset 2, were ultimately used as the internal test set and external test set, respectively, to evaluate the model’s segmentation effect. A series of optimizations were made to the original YOLOv5 algorithm, introducing the Atrous Spatial Pyramid Pooling (ASPP), Convolutional Block Attention Module (CBAM), Coordinate Attention (CA) for structural improvement, resulting in several optimized versions, namely YOLOv5s-ASPP, YOLOv5s-CBAM, YOLOv5s-CA, YOLOv5s-ASPP-CBAM, and YOLOv5s-ASPP-CA. The training and validation sets were input into the original YOLOv5s model, five optimized models, and the YOLOv8s model for 100 rounds of iterative training. The best weight file of the model with the best evaluation index in the six trained models was used for the final test of the test set.ResultsAfter iterative training, the seven models can segment and recognize brain tumor magnetic resonance images. Their precision rates on the validation set are 92.5, 93.5, 91.2, 91.8, 89.6, 90.8, and 93.1%, respectively. The corresponding recall rates are 84, 85.3, 85.4, 84.7, 87.3, 85.4, and 91.9%. The best weight file of the model with the best evaluation index among the six trained models was tested on the test set, and the improved model significantly enhanced the image segmentation ability compared to the original model.ConclusionCompared with the original YOLOv5s model, among the five improved models, the improved YOLOv5s-ASPP model significantly enhanced the segmentation ability of brain tumor magnetic resonance images, which is helpful in assisting clinical diagnosis and treatment planning.https://www.frontiersin.org/articles/10.3389/fnins.2024.1510175/fullartificial intelligenceimage segmentationbrain tumormagnetic resonanceYOLOv5s |
| spellingShingle | Tao Yang Xueqi Lu Lanlan Yang Miyang Yang Jinghui Chen Hongjia Zhao Application of MRI image segmentation algorithm for brain tumors based on improved YOLO Frontiers in Neuroscience artificial intelligence image segmentation brain tumor magnetic resonance YOLOv5s |
| title | Application of MRI image segmentation algorithm for brain tumors based on improved YOLO |
| title_full | Application of MRI image segmentation algorithm for brain tumors based on improved YOLO |
| title_fullStr | Application of MRI image segmentation algorithm for brain tumors based on improved YOLO |
| title_full_unstemmed | Application of MRI image segmentation algorithm for brain tumors based on improved YOLO |
| title_short | Application of MRI image segmentation algorithm for brain tumors based on improved YOLO |
| title_sort | application of mri image segmentation algorithm for brain tumors based on improved yolo |
| topic | artificial intelligence image segmentation brain tumor magnetic resonance YOLOv5s |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1510175/full |
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