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.
摘要：
腰椎间盘突出症是临床实践中最普遍的骨科问题之一。腰椎是运动和负重的关键关节，所以背痛可以显著影响患者的日常生活，并且容易复发。腰椎间盘突出症的发病机制复杂多样,这使得在它发生后很难识别和评估。磁共振成像（MRI）是检测损伤的最有效方法，需要医学专家连续检查以确定伤害的程度。然而,连续的检查过程耗时且容易出错。本研究提出了一种增强模型，BE-YOLOv5，用于从MRI图像中分层检测腰椎间盘突出症。要根据工作要求量身定制模型的培训，创建了一个专门的数据集。在最终校准之前对数据进行清洁和改进。获得2083个数据点的最终训练集和100个数据点的测试集。通过整合注意力机制模块增强了YOLOv5模型，ECAnet,具有3×3的卷积内核大小，用BiFPN代替其特征提取网络，并实施结构系统修剪。该模型在测试集上实现了89.7％的平均精度（mAP）和48.7帧/秒（FPS）。与更快的R-CNN相比，原始的YOLOv5和最新的YOLOv8，该模型在MRI检测和分级腰椎间盘突出症的准确性和速度方面表现更好，验证多种增强方法的有效性。所提出的模型有望用于从MRI图像诊断腰椎间盘突出症，并展示高效和高精度的性能。
