02075nas a2200289   4500000000100000000000100001008004100002260001200043653002500055653003100080653002400111653001600135653002400151100001900175700001800194700001400212700002000226700001900246700001700265700001800282245014300300856008000443300001200523490000600535520123000541022001401771       2021                            d  c09/202110aCervical Spondylosis10aMagnetic Resonance Imaging10aGenetic Programming10aTABU Search10aAutomatic Detection1 aChun-Jung Juan1 aChen-Shu Wang1 aBo-Yi Lee1 aShang-Yu Chiang1 aChun-Chang Yeh1 aDer-Yang Cho1 aWu-Chung Shen00aIntegration of Genetic Programming and TABU Search Mechanism for Automatic Detection of Magnetic Resonance Imaging in Cervical Spondylosis  uhttps://www.ijimai.org/journal/sites/default/files/2021-08/ijimai6_7_11.pdf  a109-1160 v63 aCervical 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.  a1989-1660