Abstract

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Title:: Automatic diagnosis of lumbar intervertebral disc herniation based on step-by-step target positioning

Author(s):: GONG Jiamin1; 2; YANG Hongrui1; GUO Qingqing2; JIANG Jiewei2; PAN Qiong3; MA Doudou2; GAO Yanjun4; 1. School of Communication and Information Engineering, Xian University of Posts and Telecommunications, Xian 710121, China 2. School of Electronic Engineering, Xian University of Posts and Telecommunications, Xian 710121, China 3. School of Science, Northwest Agriculture and Forestry University, Xi an 712100, China 4. Department of Medical Imaging, Xian No.3 Hospital, Xian 710071, China

Keywords:: Keywords: lumbar intervertebral disc herniation step-by-step target positioning Faster R-CNN network improved residual convolutional neural network computer-aided diagnosis system

Abstract:: Abstract: A computer-aided diagnosis method based on step-by-step target positioning (SSTP) is proposed for solving the problem of low accuracy in current methods for the automatic diagnosis of lumbar intervertebral disc herniation. Firstly, Faster R-CNN target positioning network is used to preprocess lumbar intervertebral disc images, remove ligaments and surrounding noise areas, and obtain the contour of lumbar intervertebral disc. Then, the contour of the located disc is enlarged by 3 times, and Faster R-CNN network is further applied to finely locate the focus area, thus solving the problem of inaccurate positioning due to the small focus. Finally, the focus area is input into the improved residual convolution neural network to extract high-level features and to grade the severity. The improved residual convolutional neural network (ResNet-20) improves the classifier accuracy by establishing a short-circuit mechanism. Experimental results show that the proposed method improves diagnostic accuracy of lumbar intervertebral disc herniation by 5.1% in comparison with traditional diagnostic methods.