An algorithm for cardiac disease detection based on the magnetic resonance imaging
Abstract In experiments to detect heart disease on cardiac magnetic resonance imaging (MRI) medical images, existing object detection models face several challenges including low accuracy and unreliable detection results. To tackle these issues, this article proposes an innovative method for Object...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88567-3 |
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| author | Heng Li Qingni Yuan Yi Wang Pengju Qu Chunhui Jiang Hu Kuang |
| author_facet | Heng Li Qingni Yuan Yi Wang Pengju Qu Chunhui Jiang Hu Kuang |
| author_sort | Heng Li |
| collection | DOAJ |
| description | Abstract In experiments to detect heart disease on cardiac magnetic resonance imaging (MRI) medical images, existing object detection models face several challenges including low accuracy and unreliable detection results. To tackle these issues, this article proposes an innovative method for Object Detection in cardiac MRI medical images called SA-YOLO. This method is based on the YOLOv8 model but introduces several key modifications. Firstly, the standard Spatial Pyramid Pooling Fast module is replaced with a Multi-Channel Spatial Pyramid Pooling module. Secondly, an attention mechanism combining the ideas of Squeeze-Excitation and Coordinate Attention designed, and integrated into the Neck part of the baseline model. Subsequently, the bounding box regression loss function CIoU loss of the model was replaced with the iSD-IoU loss that combines shape loss and distance loss. Finally, comparative experiments were conducted on the Automated Cardiac Diagnosis Challenge cardiac MRI image dataset where it was found that SA-YOLOv8 achieved better results in detecting cardiac pathologies, and improvement of 7.4% in mAP0.5 value and 5.1% in mAP0.5-0.95 value compared to the baseline model. |
| format | Article |
| id | doaj-art-fc7ccc920d014585b9df8a2745aa97ca |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fc7ccc920d014585b9df8a2745aa97ca2025-02-09T12:33:01ZengNature PortfolioScientific Reports2045-23222025-02-0115111810.1038/s41598-025-88567-3An algorithm for cardiac disease detection based on the magnetic resonance imagingHeng Li0Qingni Yuan1Yi Wang2Pengju Qu3Chunhui Jiang4Hu Kuang5Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversityKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversityThe First People’s Hospital of GuiyangKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversityKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversityKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversityAbstract In experiments to detect heart disease on cardiac magnetic resonance imaging (MRI) medical images, existing object detection models face several challenges including low accuracy and unreliable detection results. To tackle these issues, this article proposes an innovative method for Object Detection in cardiac MRI medical images called SA-YOLO. This method is based on the YOLOv8 model but introduces several key modifications. Firstly, the standard Spatial Pyramid Pooling Fast module is replaced with a Multi-Channel Spatial Pyramid Pooling module. Secondly, an attention mechanism combining the ideas of Squeeze-Excitation and Coordinate Attention designed, and integrated into the Neck part of the baseline model. Subsequently, the bounding box regression loss function CIoU loss of the model was replaced with the iSD-IoU loss that combines shape loss and distance loss. Finally, comparative experiments were conducted on the Automated Cardiac Diagnosis Challenge cardiac MRI image dataset where it was found that SA-YOLOv8 achieved better results in detecting cardiac pathologies, and improvement of 7.4% in mAP0.5 value and 5.1% in mAP0.5-0.95 value compared to the baseline model.https://doi.org/10.1038/s41598-025-88567-3Object detectionCardiac MRI medical imagesCardiac diseasesSpatial pyramid poolingJoint attention mechanismBounding box regression loss |
| spellingShingle | Heng Li Qingni Yuan Yi Wang Pengju Qu Chunhui Jiang Hu Kuang An algorithm for cardiac disease detection based on the magnetic resonance imaging Scientific Reports Object detection Cardiac MRI medical images Cardiac diseases Spatial pyramid pooling Joint attention mechanism Bounding box regression loss |
| title | An algorithm for cardiac disease detection based on the magnetic resonance imaging |
| title_full | An algorithm for cardiac disease detection based on the magnetic resonance imaging |
| title_fullStr | An algorithm for cardiac disease detection based on the magnetic resonance imaging |
| title_full_unstemmed | An algorithm for cardiac disease detection based on the magnetic resonance imaging |
| title_short | An algorithm for cardiac disease detection based on the magnetic resonance imaging |
| title_sort | algorithm for cardiac disease detection based on the magnetic resonance imaging |
| topic | Object detection Cardiac MRI medical images Cardiac diseases Spatial pyramid pooling Joint attention mechanism Bounding box regression loss |
| url | https://doi.org/10.1038/s41598-025-88567-3 |
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