Email this article 
Open Access
Subscription or Fee Access
Neuro-Fuzzy Approaches for Analysis and Diagnosis of Respiratory Infection Diseases: A Survey
Abstract
Chest X-ray, Computed Tomography Scan, Ultrasound Scans, etc. can be utilized for the analysis and diagnosis of respiratory tract infections. Intelligent system techniques applied in medical diagnosis require high level of accuracy and less time consumption in decision making. Although ANN and fuzzy logic have a lot of advantages but they have some disadvantages too. A neuro-fuzzy system is a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. Neuro-fuzzy is a combination of advantages of ANN and fuzzy logic.. Artificial intelligence and neuro-fuzzy is not only used in respiratory infection or lung cancer analysis but also tried to be used to diagnose thyroid disorder, diabetes, heart diseases, neuro diseases, asthma disease. This paper presents overview and compares the neuro fuzzy methods available for the analysis and diagnosis of the respiratory tract infection diseases.
Keywords
References
Zhou Zhixiang Joseph K C Kwan, “A Respiratory Tract Infection Index for SARS and Avian Influenza Outbreaks”, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005
Sheng-Ta Hsieh, Chih-Dao Chen, “An Expert System Application for Respiratory Infection Diagnostic”, 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing 2012.
Sandra Reichert,” Analysis of Respiratory Sounds: State of the Art,” Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine 2008:2 45–58.
Ulas Bagc, “Automatic Detection of Tree-in-Bud Patterns for Computer Assisted Diagnosis of Respiratory Tract Infections”, 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, August 30 - September 3, 2011
A. P. Bhagat, Mohammad Atique, “Design and Development of Systems for Image Segmentation and Content Based Image Retrieval,” IEEE Xplore Computational Intelligence and Signal Processing (CISP), 2012.
Donoho, D., Johnstone, I., “Minimax risk Ip-balls for I q - error”. Probab. Theory Relat. Fields, Vol.2, pp. 277–303 1994.
Barbu, A., “Automatic Detection and Segmentation of Axillary Lymph Nodes”, Proc. of MICCAI, pp.28–36 2010.
Bagci, U., and Li, B., “Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition”, Proc. Of IEEE ICIP., pp.1640–1643 2008.
C. V. Trappey, A. J. C. Trappey and C. S. Li, “Develop patient monitoring and support system using mobile communication and intelligent reasoning,” in Proc. Of IEEE International Conference on Systems, Man and Cybernetics (SMC 2009), pp. 1195-1200. , 2009
K. Ho, N. Tsuchiya, H. Nakajima, K. Kuramoto, S. Kobashi and Y. Hata, “Fuzzy logic approach to respiration detection by air pressure sensor,” in Proc. of IEEE International Conference on Fuzzy Systems, pp. 911-915, 2009.
S. H. Ling, Nuryani and H. T. Nguyen, “Evolved fuzzy reasoning model for hypoglycaemic detection,” in Proc. Of 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4662-4665, 2010.
S. H. Ling, H. T. Nguyen and F. H. F. Leung, “Hypoglycemia detection using fuzzy inference system with genetic algorithm,” in Proc. of 2011 IEEE International Conference on Fuzzy Systems (FUZZ 2011), pp. 2225-2231, 2011.
Elpiniki I. Papageorgiou,” Medical decision making through fuzzy computational intelligent approaches”, ISICA, CCIS 51, pp. 266–275, Springer-Verlag Berlin Heidelberg 2009.
Janikow C. Z., “Fuzzy Decision Trees Manual, free version for Fuzzy Decision Trees”, 1998.
Janikow C. Z.,” Fuzzy decision trees: issues and methods”. IEEE Trans. Systems Man Cybernet. Part B (Cybernetics), 28(1), pp. 1–14, (1998)
Ahmad Taher Azar, “Expert System Based on Neural- Fuzzy Rules for Thyroid Diseases Diagnosis”, MulGraB/BSBT/IUrC 2012, CCIS 353, pp. 94–105 Springer- Verlag Berlin Heidelberg 2012.
Cetişli, B, “Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1, 2”. Expert Systems with Applications 37(8), 6093–6101 (2010).
S.O. Olabiyisi, “A Decision Support System Model for Diagnosing Tropical Diseases Using Fuzzy Logic,” African Journal of Computing & ICT September 2011.
Chious H. Tzeng G. and Cheng D., “Evaluating sustainable fishing development strategies using fuzzy MCDM approach. Omega, 33:223-234, 2005.
Beyon M.J., “A method of aggregation in DS/ AHP for group decision-making with the non-equivalent importance of individuals in the group. Computers and Operations Research, 32:1881-1896, 2005.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.