An Approach to Medical Image Classification Using Neuro Fuzzy Logic and ANFIS Classifier
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - Issue 2013 by IJCTT Journal|
|Volume-4 Issue-3 |
|Year of Publication : 2013|
|Authors :Anant Bhardwaj, Kapil Kumar Siddhu|
Anant Bhardwaj, Kapil Kumar Siddhu"An Approach to Medical Image Classification Using Neuro Fuzzy Logic and ANFIS Classifier"International Journal of Computer Trends and Technology (IJCTT),V4(3):236-240 Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - It is a challenging task to analyze medical images because there are very minute variations & larger data set for analysis. It is a quite difficult to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. The conventional method in medicine for brain MR images classification and tumor detection is by human inspection. Fuzzy logic technique is more accurate but it fully depends on expert knowledge, which may not always available. Here we extract the feature using PCA and after that training using the ANFIS tool. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracy. Here the result confirmed that the proposed ANFIS classifier with accuracy greater than 90 percentage has potential in detecting the tumors. This paper describes the proposed strategy to medical image classification of patient’s MRI scan images of the brain.
 L.I. Kuncheva, F. Steimann, “Fuzzy diagnosis,” Artificial Intelligence in Medicine, vol. 16, pp. 121-128, 1999.
 J.-S.R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23(3), pp. 665-685, 1993.
 S.Y. Belal, A.F.G. Taktak, A.J. Nevill, S.A. Spencer, D. Roden, S. Bevan, “Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system,” Artificial Intelligence in Medicine, vol. 24, pp. 149-165, 2002.
 I. Virant-Klun, J. Virant, “Fuzzy logic alternative for analysis in the biomedical sciences,” Computers and Biomedical Research, vol. 32, pp. 305-321, 1999.
 Li.Y and Chi.Z.: MR Brain Image Segmentation Based on Self-Organizing Map Network, International Journal of Information Technology, vol. 11, no. 8, 2005.
Keywords— ANFIS, Brain tumor, MRI images, Brain MRI, Neuro fuzzy logic, PCA.