基于优化Yolov5的腰椎医学图像检测算法

Monitoring,management and control of self-organized network

腰椎MRI检查可将对应节段腰椎的病理变化显现出,医务人员了解病变范围、程度,有助于选择合适的手术方案。假如患者遗留腰椎间盘突出的问题等,可能会影响到手术;对腰椎间盘突出患者进行手术治疗,需进行相关腰椎MRI检查,可提高手术治愈率。本文提出基于Yolov5的腰椎图像检测算法,利用CutMix数据增强,添加CA注意力模块,来改善模型性能。通过优化的Yolov5检测,将大量腰椎MRI图像变得直观可视,使医务人员的临床工作效率得到提升,对社会贡献有着重大的意义。实验结果显示,本文提供的算法能够有效、精确、快速地检测腰椎MRI图像。

MRI examination of the lumbar spine can reveal the pathological changes of the corresponding segment of the lumbar spine,and medical personnel can understand the extent of the lesion,which is helpful in selecting an appropriate surgical plan.If the patient has left behind issues such as lumbar disc herniation,it may affect the surgery;Surgical treatment for patients with lumbar disc herniation requires relevant lumbar MRI examination to improve the surgical cure rate.This article proposes a lumbar spine image detection algorithm based on YOLOv5,which utilizes CutMix data augmentation and adds a CA attention module to improve model performance.The experimental results show that the algorithm provided in this article can effectively,accurately,and quickly detect lumbar MRI images.