Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and Industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. Despite excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthening the accuracy of results. Indicators such as area and Intersection over Union (IoU)are also provided for assessment. Among them, the mean average precision (mAP), for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

Artificial intelligence is a computer technology that has attracted considerable attention in recent years. As computer equipment has become increasingly advanced, deep learning in the field of artificial intelligence has made breakthroughs. In recent years, the use of deep learning in the medical field has contributed to many issues of efficiency and accuracy. Regardless of the excellent results in radiology, pathology, endoscopy, ultrasound, and biochemical examination, in this study, we used deep learning as a tool to identify spinal canals and spinal foramen stenosis. Based on previous studies, new techniques, such as erosion and expansion, were added to make the results more accurate, and provide area, IoU and other indicators for evaluation. This paper not only applies the latest computer science and information engineering technology--deep learning to medicine, but also uses a breakthrough network model to improve the recognition effect.Use quantitative indicators to make results more reliable. In this study, we compared two network architecture, Resnet50 and VGG16, to identify the spinal canal and intervertebral foramen. Resnet50 had quite good results in IoU, which were 77.4% and 80.9% in spinal canal and intervertebral foramen, respectively.