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Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI.
- Source :
-
Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Dec; Vol. 62 (12), pp. 3709-3719. Date of Electronic Publication: 2024 Jul 05. - Publication Year :
- 2024
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Abstract
- Lumbar disc herniation is one of the most prevalent orthopedic issues in clinical practice. The lumbar spine is a crucial joint for movement and weight-bearing, so back pain can significantly impact the everyday lives of patients and is prone to recurring. The pathogenesis of lumbar disc herniation is complex and diverse, making it difficult to identify and assess after it has occurred. Magnetic resonance imaging (MRI) is the most effective method for detecting injuries, requiring continuous examination by medical experts to determine the extent of the injury. However, the continuous examination process is time-consuming and susceptible to errors. This study proposes an enhanced model, BE-YOLOv5, for hierarchical detection of lumbar disc herniation from MRI images. To tailor the training of the model to the job requirements, a specialized dataset was created. The data was cleaned and improved before the final calibration. A final training set of 2083 data points and a test set of 100 data points were obtained. The YOLOv5 model was enhanced by integrating the attention mechanism module, ECAnet, with a 3 × 3 convolutional kernel size, substituting its feature extraction network with a BiFPN, and implementing structural system pruning. The model achieved an 89.7% mean average precision (mAP) and 48.7 frames per second (FPS) on the test set. In comparison to Faster R-CNN, original YOLOv5, and the latest YOLOv8, this model performs better in terms of both accuracy and speed for the detection and grading of lumbar disc herniation from MRI, validating the effectiveness of multiple enhancement methods. The proposed model is expected to be used for diagnosing lumbar disc herniation from MRI images and to demonstrate efficient and high-precision performance.<br />Competing Interests: Declarations Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Conflict of interest All the authors declare that they have no conflict of interest.<br /> (© 2024. International Federation for Medical and Biological Engineering.)
Details
- Language :
- English
- ISSN :
- 1741-0444
- Volume :
- 62
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- Medical & biological engineering & computing
- Publication Type :
- Academic Journal
- Accession number :
- 38967693
- Full Text :
- https://doi.org/10.1007/s11517-024-03161-5