Open AccessDetection of spinal fracture lesions based on Improved Yolov3
Author(s) -
Gang Sha,
Junsheng Wu,
Bin Yu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1576/1/012016
Subject(s) - artificial intelligence , intersection (aeronautics) , computer science , minimum bounding box , computer vision , fracture (geology) , transformation (genetics) , deep learning , process (computing) , lumbar , image (mathematics) , radiology , medicine , geology , engineering , biochemistry , chemistry , geotechnical engineering , gene , aerospace engineering , operating system
Yolo[1]algorithm has a good detection effect in target detection. Because of its high detection accuracy and fast detection speed, it is widely used in practice. Because of the problem that the complexity of spine CT images, the irregular shape of vertebral boundary, which needs doctors’ prior knowledge and clinical experience to determine lesions location in CT images, so it can not meet the clinical real-time needs. In this paper, We use deep learning to process the CT images of spine, and to detect and locate lesion of (cervical fracture, cfracture), (thoracic fracture, tfracture), (lumbar fracture, lfracture) by the improved YOLOv3. Through using lesions bounding box dimensional cluster, multiscale transformation of input CT images, and change NMS to MAX value to improve detection efficiency and accuracy. The experiment shows the results are more accurate, and mAP (mean average precision) of detection algorithm is 73.63%, detection rate is 0.027 seconds per detection, and IOU((Intersection-over-Union) is 75.9, which can basically meet the clinical real-time needs.