段老师在 Med Bio Eng Comput 期刊发表论文

段老师参与完成的题目为“Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI”的论文被期刊Medical & Biological Engineering & Computing接收，段老师为论文共同通讯作者。

本文提出了一种改进的模型BE-YOLOv5，用于从MRI图像中分级检测腰椎间盘突出。为了适应模型训练的需求，研究创建了一个专门的数据集，并对数据进行了清理和改进，最终获得2083个训练数据点和100个测试数据点。本文通过引入ECAnet注意力机制模块（使用3×3卷积核大小）、将特征提取网络替换为BiFPN，并实施结构系统剪枝，增强了YOLOv5模型。改进后的模型在测试集上达到了89.7%的平均精确度（mAP）和48.7帧每秒（FPS）。与Faster R-CNN、原始YOLOv5和最新的YOLOv8相比，该模型在检测和分级腰椎间盘突出方面在准确性和速度上均表现更好，验证了多种增强方法的有效性。所提出的模型有望用于诊断MRI图像中的腰椎间盘突出，并展示高效且高精度的性能。

xhl.png

该文章基本信息如下：

  • 标题：Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI
  • 作者：Xianyin Duan, Hanlin Xiong, Rong Liu*, Xianbao Duan*, Haotian Yu
  • 摘要：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.
  • 期刊：Medical & Biological Engineering & Computing
  • 链接：https://link.springer.com/article/10.1007/s11517-024-03161-5