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Type-2 Fuzzy Hybrid Expert System For Diagnosis Of Degenerative Disc Diseases | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 6، دوره 45، شماره 2، بهمن 2013، صفحه 53-62 اصل مقاله (763.44 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2015.529 | ||
| نویسندگان | ||
| S. Rahimi Damirchi-Darasi1؛ M.H. Fazel Zarandi* 2؛ M. Izadi3 | ||
| 1Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 2Knowledge Intelligent System Laboratory, University of Toronto, Toronto, Canada | ||
| 3Sub-special Neurosurgery, Fayyazbakhsh and Erfan Hospital, Tehran, Iran | ||
| چکیده | ||
| One-third of the people with an age over twenty have some signs of degenerated discs. However, in most of the patients the mere presence of degenerative discs is not a problem leading to pain, neurological compression, or other symptoms. This paper presents an interval type-2 fuzzy hybrid rule-based system to diagnose the abnormal degenerated discs where pain variables are represented by interval type-2 membership functions. For this purpose, Mamdani interval type-2 fuzzy sets are utilized in the inference engine. The main contribution of this paper is to present the interval type-2 fuzzy hybrid rule-based system, which is the combination of forward and backward chaining approach in its inference engine. Combining forward and backward chaining leads to detect the exact location of degenerated disc that shows some spinal instability. The phase of forward chaining diagnoses the severity of the degeneration based on taking history of the patient. The second phase uses backward chaining approach to find the exact location of the degenerated disc by investigating related clinical examinations. Using parametric operations for the fuzzy calculations increases the robustness of the system. The system is tested for 11 patients and the results are compared with the neurosurgeon’s diagnosis. Results indicate that the hybrid of forward and backward chaining approaches provide fast and accurate diagnosis of degenerative disc disease, and determine the necessity of taking MRI. Concluding, the proposed system could be a valuable tool in hand of the physicians in clinics and imaging centers to support diagnosis of the degenerated discs. | ||
| کلیدواژه‌ها | ||
| Type-2 Fuzzy Expert System؛ Forward-Backward Chaining؛ Degenerative Disc Diseases؛ Diagnosis System | ||
| عنوان مقاله [English] | ||
| Type-2 Fuzzy Hybrid Expert System For Diagnosis Of Degenerative Disc Diseases | ||
| نویسندگان [English] | ||
| S. Rahimi Damirchi-Darasi1؛ M.H. Fazel Zarandi2؛ M. Izadi3 | ||
| 1Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 2Knowledge Intelligent System Laboratory, University of Toronto, Toronto, Canada | ||
| 3Sub-special Neurosurgery, Fayyazbakhsh and Erfan Hospital, Tehran, Iran | ||
| چکیده [English] | ||
| One-third of the people with an age over twenty have some signs of degenerated discs. However, in most of the patients the mere presence of degenerative discs is not a problem leading to pain, neurological compression, or other symptoms. This paper presents an interval type-2 fuzzy hybrid rule-based system to diagnose the abnormal degenerated discs where pain variables are represented by interval type-2 membership functions. For this purpose, Mamdani interval type-2 fuzzy sets are utilized in the inference engine. The main contribution of this paper is to present the interval type-2 fuzzy hybrid rule-based system, which is the combination of forward and backward chaining approach in its inference engine. Combining forward and backward chaining leads to detect the exact location of degenerated disc that shows some spinal instability. The phase of forward chaining diagnoses the severity of the degeneration based on taking history of the patient. The second phase uses backward chaining approach to find the exact location of the degenerated disc by investigating related clinical examinations. Using parametric operations for the fuzzy calculations increases the robustness of the system. The system is tested for 11 patients and the results are compared with the neurosurgeon’s diagnosis. Results indicate that the hybrid of forward and backward chaining approaches provide fast and accurate diagnosis of degenerative disc disease, and determine the necessity of taking MRI. Concluding, the proposed system could be a valuable tool in hand of the physicians in clinics and imaging centers to support diagnosis of the degenerated discs. | ||
| کلیدواژه‌ها [English] | ||
| Type-2 Fuzzy Expert System, Forward-Backward Chaining, Degenerative Disc Diseases, Diagnosis System | ||
| مراجع | ||
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[1] “Conditions Treated Degenerative Disc Disease,” Available:http://neurosurgery.ucla.edu/body.cfm?id=1123&ref=111&action=detail, Feb. 24. [2] J. P.Revord. “Typical Symptoms of a Herniated Disc,” Available: http://www.spine-health.com/conditions/herniated-disc/typical-symptoms-a-herniated-disc, Feb. 17. [3] R. Staehler. “Cervical Herniated Disc Symptoms and Treatment Options,” Available: http://www.spine-health.com/conditions/herniated-disc/cervical-herniated-disc-symptoms-and-treatment-options. Feb. 04. [4] J. Koh, V. Chaudhary, E. K. Jeon, and G. Dhillon, "Automatic spinal canal detection in lumbar MR images in the sagittal view using dynamic programming," Computerized Medical Imaging and Graphics, vol. 38, pp. 569-579, 10//. [5] R. Alomari, J. Corso, V. Chaudhary, and G. Dhillon, "Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model," in Computational Methods and Clinical Applications for Spine Imaging. vol. 17, J. Yao, T. Klinder, and S. Li, Eds., ed: Springer International Publishing, pp. 87-98. [6] J. Koh, V. Chaudhary, and G. Dhillon, "Disc herniation diagnosis in MRI using a CAD framework and a two-level classifier," Int J Comput Assist Radiol Surg, vol. 7, pp. 861-9, Nov 2012. [7] S. Ghosh, R. S. Alomari, V. Chaudhary, and G. Dhillon, "Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI," Conf Proc IEEE Eng Med Biol Soc, vol. 2011, pp. 5068-71, 2011. [8] S. Ghosh, R. S. Alomari, V. Chaudhary, and G. Dhillon, "Computer-aided diagnosis for lumbar mri using heterogeneous classifiers," in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pp. 1179-1182, 2011. [9] R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon, "Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI," Int J Comput Assist Radiol Surg, vol. 5, pp. 287-93, May 2010. [10] S. K. Michopoulou, L. Costaridou, E. Panagiotopoulos, R. Speller, G. Panayiotakis, and A. Todd-Pokropek, "Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine," IEEE Trans Biomed Eng, vol. 56, pp. 2225-31, Sep 2009. [11] M. Toth-Tascau, D. I. Stoia, and D. Andrei, "Integrated methodology for a future expert system used in low back pain management," in Applied Computational Intelligence and Informatics (SACI), 2012 7th IEEE International Symposium on, pp. 315-320, 2012. [12] O. A. R. M. a. O. A. D. Kandil, "The use of Knowledge Based Expert System Approach in Examining Causes of Low Back Pain in Computer users," Eur. J. Sci. Res, vol. 50, pp. 352-362, March 2011. [13] M. A. Kadhim, M. A. Alam, and H. Kaur, "Design and Implementation of Fuzzy Expert System for Back pain Diagnosis," International Journal of Innovative Technology & Creative Engineering, IJITCE, vol. U, pp. 16-22, 2011. [14] M. Sari, E. Gulbandilar, and A. Cimbiz, "Prediction of low back pain with two expert systems," J Med Syst, vol. 36, pp. 1523-7, Jun 2012. [15] MatheMEDics. "EasyDiagnosis Modules," Internet: http://easydiagnosis.comlmodules.html, Jan. 20, 2012 [Feb. 08, 2012]. [16] J.M. Mendel, R.I. John, “Type-2 fuzzy sets made simple,” Fuzzy Systems, IEEE Transactions on, 2002. [17] J.M. Mendel, R.I. John, F. Liu, Interval type-2 fuzzy logic systems made simple, IEEE Transactions on Fuzzy Systems vol. 14, no.6, pp. 808–821, 2006. [18] J.M. Mendel, Type-2 fuzzy sets and systems: an overview, IEEE Computational Intelligence Magazine, vol. 2, no. 1, pp. 20–29, 2007. [19] I. B. Turksen, "Type 2 representation and reasoning for CWW," Fuzzy Sets and Systems, vol. 127, pp. 17-36, 4/1/ 2002. [20] D. Wu, W.W. Tan, “A type-2 fuzzy logic controller for the liquid-level process,” in: Proc. IEEE International Conference on Fuzzy Systems, Hungary, pp. 953–958, 2004. [21] M.H. Fazel Zarandi, S.R. Damirchi-Darasi, M. Izadi, I.B. Turksen, M.A. Ghahazi, "Fuzzy rule based expert system to diagnose spinal cord disorders," in Norbert Wiener in the 21st Century (21CW), IEEE Conference on , vol., no., pp.1-5, 24-26 June. | ||
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