Integration of Genetic Programming and TABU Search Mechanism for Automatic Detection of Magnetic Resonance Imaging in Cervical Spondylosis.

Chun Jung Juan; Chen Shu Wang; Bo Yi Lee; Shang Yu Chiang; Chun Chang Yeh; Der Yang Cho; Wu Chung Shen

doi:10.9781/ijimai.2021.08.006

Authors

Chun Jung Juan China Medical University
Chen Shu Wang University of Technology
Bo Yi Lee National Taipei University
Shang Yu Chiang National Taipei University
Chun Chang Yeh National Defense Medical Center
Der Yang Cho China Medical University Hospital
Wu Chung Shen China Medical University

DOI:

https://doi.org/10.9781/ijimai.2021.08.006

Keywords:

Cervical Spondylosis, Magnetic Resonance Imaging, Genetic Programming, TABU Search, Automatic Detection

Supporting Agencies

Chen-Shu Wang received financial support of the Ministry of Science and Technology (Grant number: MOST109-2221-E027-072), Taiwan, R.O.C. Chun-Chang Yeh and Shang-Yu Chiang received financial support of Tri-Service General Hospital (Grant number: TSGH-C106-086) for this work. Chun-Jung Juan, Wu-Chung Shen and Der-Yang Cho received funding support partly from the China Medical University Hospital (Grant number: CMUH-DMR-108-056).

Abstract

Cervical spondylosis is a kind of degenerative disease which not only occurs in elder patients. The age distribution of patients is unfortunately decreasing gradually. Magnetic Resonance Imaging (MRI) is the best tool to confirm the cervical spondylosis severity but it requires radiologist to spend a lot of time for image check and interpretation. In this study, we proposed a prediction model to evaluate the cervical spine condition of patients by using MRI data. Furthermore, to ensure the computing efficiency of the proposed model, we adopted a heuristic programming, genetic programming (GP), to build the core of refereeing engine by combining the TABU search (TS) with the evolutionary GP. Finally, to validate the accuracy of the proposed model, we implemented experiments and compared our prediction results with radiologist’s diagnosis to the same MRI image. The experiment found that using clinical indicators to optimize the TABU list in GP+TABU got better fitness than the other two methods and the accuracy rate of our proposed model can achieve 88% on average. We expected the proposed model can help radiologists reduce the interpretation effort and improve the relationship between doctors and patients.

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References

B. M. McCormack, and P. R. Weinstein, “Cervical spondylosis. An update,” western Journal of Medicine, vol. 165, no. 1-2, pp. 43, 1996, PMCID: PMC1307540.

C. Wang, F. Tian, Y. Zhou, W. He, and Z. Cai, “The incidence of cervical spondylosis decreases with aging in the elderly, and increases with aging in the young and adult population: A hospital-based clinical analysis,” Clinical interventions in aging, vol. 11, pp. 47, 2016, doi: 10.2147/CIA.S93118.

A. I. Binder, “Cervical spondylosis and neck pain,” Bmj, vol. 334, no. 7592, pp. 527-531, 2007, doi: 10.1136/bmj.39127.608299.80.

L. Brain and M. Wilkinson, Ed., Cervical spondylosis and other disorders of the cervical spine, Oxford, United Kingdom: Butterworth-Heinemann, 2013.

S. Y. Kim and S. J. Koo, “Effect of duration of smartphone use on muscle fatigue and pain caused by forward head posture in adults,” Journal of physical therapy science, vol. 28, pp. 1669-1672, 2016, doi: 10.1589/jpts.28.1669.

S. P. Cohen, “Epidemiology, diagnosis, and treatment of neck pain,” Mayo Clinic Proceedings, vol. 90, no. 2, pp. 284-299, 2015, doi: 10.1016/j.mayocp.2014.09.008.

Y. G. Kim, M. H. Kang, J. W. Kim, J. H. Jang, and J. S. Oh, “Influence of the duration of smartphone usage on flexion angles of the cervical and lumbar spine and on reposition error in the cervical spine,” Physical Therapy Korea, vol. 20, pp. 10-17, 2013, doi: 10.12674/ptk.2013.20.1.010.

M. Terry, “Campbell’s operative orthopedics,” The Journal of the American Medical Association, vol. 301, no. 3, pp. 329-330, 2009, doi: 10.1001/jama.2008.969.

Y. Jiang, A. V. Edwards, and G. M. Newstead, “Artificial intelligence applied to breast MRI for improved diagnosis,” Radiology, vol. 298, no. 1, pp. 38-46, 2021, doi: 10.1148/radiol.2020200292.

L. Lin, Q. Dou, Y. M. Jin, G. Q. Zhou, Y. Q.Tang, W. L. Chen, ... and Y. Sun, “Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma,” Radiology, vol. 291, no. 3, pp. 677- 686, 2019, doi: 10.1148/radiol.2019182012.

A. Paul, A. Paul, & P. B. Chanda, “Detection and Classification of Cervical Spondylosis Using Image Segmentation Techniques,” In Proceedings of Second National Conference, Springer, Singapore, pp. 145-154.

L. Zhang, , & H. Wang, “A novel segmentation method for cervical vertebrae based on PointNet++ and converge segmentation,” Computer Methods and Programs in Biomedicine, vol. 200, 105798, 2021, doi: 10.1016/j.cmpb.2020.105798.

C. S. Wang, C. J. Juan, T. Y. Lin, C. C. Yeh, and S. Y. Chiang, “Prediction Model of Cervical Spine Disease Established by Genetic Programming,” In Proceedings of the 4th Multidisciplinary International Social Networks Conference, New York, United States, pp. 1-6.

J. Ma, & X. Gao, “A filter-based feature construction and feature selection approach for classification using Genetic Programming,” Knowledge-Based Systems, vol. 196, 105806, 2020, doi: 10.1016/j.knosys.2020.105806.

N. Rokbani, R. Kumar, A. Abraham, A. M. Alimi, H. V. Long & I. Priyadarshini, “Bi-heuristic ant colony optimization-based approaches for traveling salesman problem,” Soft Computing, vol. 25, pp. 3775–3794, 2021, doi: 10.1007/s00500-020-05406-5.

R. S. Sexton, B. Alidaee, R. E. Dorsey, and J. D. Johnson, “Global optimization for artificial neural networks: A tabu search application,” European Journal of Operational Research, vol. 106, no. 2-3, pp. 570-584, 1998, doi: 10.1016/S0377-2217(97)00292-0.

T. Hou, J. Wang, L. Chen, and X. Xu, “Automated docking of peptides and proteins by using a genetic algorithm combined with a tabu search,” Protein Engineering, vol. 12, no. 8, pp. 639-648, 1999, doi: 10.1093/ protein/12.8.639.

Q. Shen, W.-M. Shi, and W. Kong, “Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data,” Computational Biology and Chemistry, vol. 32, no. 1, pp. 53-60, 2008, doi: 10.1016/j.compbiolchem.2007.10.001.

X. Zhang, T. Wang, H. Luo, J. Y. Yang, Y. Deng, J. Tang, et al., “3D Protein structure prediction with genetic tabu search algorithm,” BMC systems biology, vol. 4, S6, 2010, doi: 10.1186/1752-0509-4-S1-S6.

N. R. Council, Ed., Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease, D.C., USA: National Academies Press, 2011.

J. C. W. Lin, Y. Shao, Y. Djenouri, & U. Yun, “ASRNN: a recurrent neural network with an attention model for sequence labeling,” Knowledge-Based Systems, vol. 212, 106548, 2021, doi: 10.1016/j.knosys.2020.106548.

J. C. W. Lin, G. Srivastava, Y. Zhang, Y. Djenouri, & M. Aloqaily, “Privacy preserving multi-objective sanitization model in 6G IoT environments,” IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5340–5349, 2021, doi: 10.1109/JIOT.2020.3032896.

Q. Nie, Y. B. Zou, & J. C. W. Lin, “Feature extraction for medical ct images of sports tear injury,” Mobile Networks and Applications, vol. 26, pp. 404–414, 2021, doi: 10.1007/s11036-020-01675-4.