Spinal cord segmentation and injury detection using a Crow Search-Rider optimization algorithm
Author:
Jasim Munavar1, Brindha Thomas2
Affiliation:
1. Noorul Islam Centre For Higher Education , Thuckalay, Kumaracoil, Kanyakumari, 629180, India 2. Department of Information Technology, Noorul Islam Centre For Higher Education , Thuckalay, Kumaracoil, Kanyakumari , 629180, Tamil Nadu , India
Abstract
Abstract
The damage in the spinal cord due to vertebral fractures may result in loss of sensation and muscle function either permanently or temporarily. The neurological condition of the patient can be improved only with the early detection and the treatment of the injury in the spinal cord. This paper proposes a spinal cord segmentation and injury detection system based on the proposed Crow search-Rider Optimization-based DCNN (CS-ROA DCNN) method, which can detect the injury in the spinal cord in an effective manner. Initially, the segmentation of the CT image of the spinal cord is performed using the adaptive thresholding method, followed by which the localization of the disc is performed using the Sparse FCM clustering algorithm (Sparse-FCM). The localized discs are subjected to a feature extraction process, where the features necessary for the classification process are extracted. The classification process is done using DCNN trained using the proposed CS-ROA, which is the integration of the Crow Search Algorithm (CSA) and Rider Optimization Algorithm (ROA). The experimentation is performed using the evaluation metrics, such as accuracy, sensitivity, and specificity. The proposed method achieved the high accuracy, sensitivity, and specificity of 0.874, 0.8961, and 0.8828, respectively that shows the effectiveness of the proposed CS-ROA DCNN method in spinal cord injury detection.
Publisher
Walter de Gruyter GmbH
Subject
Biomedical Engineering
Reference36 articles.
1. Raghavendra, U, Bhat, NS, Gudigar, A, Acharya, UR. Automated system for the detection of thoracolumbar fractures using a CNN architecture. Future Generat Comput Syst 2018;85:184–9. https://doi.org/10.1016/j.future.2018.03.023. 2. Al-Nashash, H, Fatoo, NA, Mirza, NN, Ahmed, RI, Agrawal, G, Nitish, V, et al.. Spinal cord injury detection and monitoring using spectral coherence. IEEE Trans Biomed Eng 2009;56:1971–9. https://doi.org/10.1109/tbme.2009.2018296. 3. Leener, BD, Taso, M, Cohen-Adad, J, Callot, V. Segmentation of the human spinal cord. Magn Reson Mater Phys Biol Med 2016;29:125–53. 4. Lee, J, Kim, S, Kim, YS, Chung, WK. Automated segmentation of the lumbar pedicle in ct images for spinal fusion surgery. IEEE Trans Biomed Eng 2011;58:2051–63. https://doi.org/10.1109/TBME.2011.2135351. 5. Cinque, B, Torre, CL, Lombardi, F, Palumbo, P, Evtoski, Z, Santini, S, et al.. VSL# 3 probiotic differently influences IEC‐6 intestinal epithelial cell status and function. J Cell Physiol 2017;232:3530–9. https://doi.org/10.1002/jcp.25814.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|