دانشگاه آزاد اسلامی واحد علوم و تحقیقات

 آمار

تعداد نشریات50
تعداد شماره‌ها2,204
تعداد مقالات20,224
تعداد مشاهده مقاله24,112,203
تعداد دریافت فایل اصل مقاله22,065,179
Hosseini, Rahil, Latifi, Farzaneh, Mazinani, Mahdi. (1395). A Fuzzy-GA Approach for Parameter Optimization of A Fuzzy Expert System for Diagnosis of Acute Lymphocytic Leukemia in Children. , 2(2), 33-42.
Rahil Hosseini; Farzaneh Latifi; Mahdi Mazinani. "A Fuzzy-GA Approach for Parameter Optimization of A Fuzzy Expert System for Diagnosis of Acute Lymphocytic Leukemia in Children". , 2, 2, 1395, 33-42.
Hosseini, Rahil, Latifi, Farzaneh, Mazinani, Mahdi. (1395). 'A Fuzzy-GA Approach for Parameter Optimization of A Fuzzy Expert System for Diagnosis of Acute Lymphocytic Leukemia in Children', , 2(2), pp. 33-42.
Hosseini, Rahil, Latifi, Farzaneh, Mazinani, Mahdi. A Fuzzy-GA Approach for Parameter Optimization of A Fuzzy Expert System for Diagnosis of Acute Lymphocytic Leukemia in Children. , 1395; 2(2): 33-42.

A Fuzzy-GA Approach for Parameter Optimization of A Fuzzy Expert System for Diagnosis of Acute Lymphocytic Leukemia in Children

Journal of Advances in Computer Engineering and Technology
مقاله 4، دوره 2، شماره 2 - شماره پیاپی 6، مرداد 2016، صفحه 33-42  XML اصل مقاله (520.23 K)
نوع مقاله: Original Research Paper
نویسندگان
Rahil Hosseini email 1؛ Farzaneh Latifi2؛ Mahdi Mazinani3
1Islamic Azad University, Shahr-e-Qods Branch, Tehran, Iran
2Islamic Azad University, Shahr-e-Qods branch
3Islamic Azad University, Shahr-e-Qods branch, Tehran, Iran
چکیده
Hybrid fuzzy expert systems are one of the most practical intelligent paradigm of soft computing techniques with the high potential for managing uncertainty associated to the medical diagnosis. The potential of genetic algorithm (GA) by inspiring from natural evolution as a learning and optimization technique has been vastly concentrated for improving fuzzy expert systems. In this paper, the GA capabilities have been applied for optimization of the membership function parameters in a fuzzy inference system (FIS) for diagnosing of acute lymphocytic leukemia in children. The fuzzy expert system utilizes the high interpretability of the Mamdani reasoning model to explain system results to experts in a high level and combines it with the GA optimization capability to improve its performance. The hybrid proposed Fuzzy-GA approach was implemented in Matlab software and evaluated on the real patients’ dataset. High accuracy of this system was achieved after GA tuning process with an accuracy about 98%. The results reveal the hybrid fuzzy-GA approach capability to assist computer-based diagnosis of medical experts, and consequently early diagnosis of the disease which is promising for providing suitable treatment for patients and saving more children’s lives.
کلیدواژه‌ها
Fuzzy expert system؛ Genetic algorithm؛ acute lymphocytic leukemia؛ computer aided diagnosis of leukemia
مراجع
[1] Shortliffe EH, Cimino JJ. Biomedical informatics: computer applications in health care and biomedicine. New York: Springer; 2006.
[2] Durkin J., Expert Systems: Design and Development, Prentice Hall, New York, 1994.
[3] Edward A, Feigenbaum BG, Buchanan D, and Meta D, Roots of Knowledge Systems and Expert System Applications, Artificial Intelligent 59 (1933), no. 1-2, 233-240.
[4] Shorrtliffe EH, Computer-based Medical Consultations: MYCIN, Elsevier Science Publishers, New York (1976).
[5] Roger Jang, Neuro-Fuzzy and Soft Computing ,Chapter 1&3& 4 , Introduction to Fuzzy Systems &Fuzzy Rules and Fuzzy reasoning &Fuzzy Inference System FIS , 1997.
[6] ArminGhasemAzar 1 Zohreh Mohammad Alizadeh 2 Department of Computer Science 1,2 , International Journal Of Computer & Information Technologies (IJOCIT) ISSN= 2345-3877 ,2013 Designing an Expert Systemto Diagnose and Propose about Therapy of Leukemia.
[7] H. IslamiNosratAbadi, S. Mohsen Taheri, A Fuzzy Expert System for Diagnosis of Blood Cancer, in the proceeding of 6th International Conference of ICT Management.
[8] Acute lymphocytic leukemia(ALL), Children's Cancer Research Fund, available at : http://www.childrenscancer.org
[9] America cancer society, www.cancer.org (-800-227-2345), 2015.
[10] NurAlom Talukdar 1 Daizy Deb 2 Sudipta Roy3 , “International Journal Of Advanced Research in Computer Science and Software Engineering “ ISSN:2277 128X ,Automated Blood Cancer Detection Using Image Processing Based on Fuzzy System.
[11] AdewunmiM.Tosin 1 Adekunle Y.A 2 , “ International Journal of Advanced Research in Computer Science and Software Engeneering “.ISSN:2277 128X,Clinical Decision Support System for Diagnosis of Pneumonia in children, 2013.
[12] J.C. ,Imianvan A.A , “ GLOBAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY“,ISSN:0975-4350, Interactive Neuro-Fuzzy Expert System for Diagnosis of Leukemia ,2011.
[13] Roger Jang, Neuro-Fuzzy and Soft Computing ,Chapter 1&3& 4 , Introduction to Fuzzy Systems &Fuzzy Rules and Fuzzy reasoning &Fuzzy Inference System FIS , 1997.
[14] R. Hosseini, S. D. Qanadli, S. Barman, M. Mazinani, T.Ellis, and J. Dehmeshki, An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System, IEEE Transactions on Fuzzy Systems, vol. 20, No. 2, April, pp. 224-234 (2012).
[15] R. Khezri, R. Hosseini and M. Mazinani, A Fuzzy Rule-Based Expert System for The Prognosis of The Risk of Development of The Breast Cancer, International Journal of Engineering Transactions A: Basics, vol. 27, No. 10, pp. 1557-1564, (October 2014).
[16] MalihehNoorizadeh, Rahil Hosseini, AcuteLeukaemia Diagnosisusing A Fuzzy Expert System, In Proc. of the 14th Fuzzy SystemsAssociation Conference, Tabriz, Iran, August 2014.
[17] Rahil Hosseini, Mahdi Mazinani, In Proc. of 9th Symposium of Advances in Science and Technology, Classification of Uncertainty Sources in Intelligent Image Processing and Analysis Systems, November 2014, Mashhad, Iran.
[18] Jaun M. Corchado 1 Jaun F. Depaz 2 Sara Rodriguez 3 Javier Bajo, Artificial Intelligence in Medicine 46 ,179-200 , Model of experts fordicision support in the diagnosis of leukemia patients,2009.
[19] DeveshD.Nawgaje 1Dr.Rajendra D.Kanphade 2, “International Journal of Engineering Science and Technology (IJEST)”,Implimentation of Fuzzy Inference System for White Blood Cell Cancer Detection Using DSP TMS320C6711.
[20] AzimMehrvar, Mahak Hospital Manager, Interview ,JameJAmNewsPaer, May 2012.
[21] P. Ganesh Kumar a , T. Aruldoss Albert Victoire b, P. Renukadevi c, D. Devaraj d, Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm, Expert Systems with Applications 39 (2012) 1811-1821,Elsevier
[22] Shutao Li · Xixian Wu · Xiaoyan Hu, Gene selection using genetic algorithm and support vectors machines , Soft Computing (2008) 12:693–698 ,DOI 10.1007/s00500-007-0251-2 , Springer-Verlag 2008
[23] Mohammad Rezaee and Mohammad Bagher Tavakoli, Cancerous Masses Segmentation by Using Heuristic Ant Colony Algorithms in Medical Images , “Journal of Image and Graphics, Volume 2, No.2, December 2014”
[24] Shutao Li · Xixian Wu · Mingkui Tan, Gene selection using hybrid particle swarm optimization and genetic algorithm, Soft Computing (2008) 12:1039–1048, Springer
[25] Juan Liu,Hitoshi Iba , Selecting Informative Genes with Parallel Genetic Algorithms in Tissue Classification, Genome Informatics 12: 14–23 (2001)
[26] Enrique Alba, Jos´e Garc´ıa-Nieto, Laetitia Jourdan and El-Ghazali Talbi, Gene Selection in Cancer Classification using PSO/SVM and GA/SVM Hybrid Algorithms, 2008.

آمار
تعداد مشاهده مقاله: 1,222
تعداد دریافت فایل اصل مقاله: 2,984


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب