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Symmetry 2017, 9(3), 37; doi:10.3390/sym9030037

Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System

Faculty of Engineering, Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Department of Electrical Engineering, Faculty of Engineering, COMSATS Institute of Information Technology, Islamabad 45550, Pakistan
Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Department of Computer Science, Sukkur Institute of Business Administration, Sukkur 65200, Pakistan
Department of Computer Science and Engineering, Faculty of Science and Engineering, EastWest University, Dhaka 1212, Bangladesh
Authors to whom correspondence should be addressed.
Academic Editor: Angel Garrido
Received: 29 November 2016 / Revised: 23 February 2017 / Accepted: 1 March 2017 / Published: 8 March 2017
(This article belongs to the Special Issue Symmetry in Complex Networks II)
View Full-Text   |   Download PDF [1620 KB, uploaded 17 March 2017]   |  


Background: An accurate and automatic computer-aided multi-class decision support system to classify the magnetic resonance imaging (MRI) scans of the human brain as normal, Alzheimer, AIDS, cerebral calcinosis, glioma, or metastatic, which helps the radiologists to diagnose the disease in brain MRIs is created. Methods: The performance of the proposed system is validated by using benchmark MRI datasets (OASIS and Harvard) of 310 patients. Master features of the images are extracted using a fast discrete wavelet transform (DWT), then these discriminative features are further analysed by principal component analysis (PCA). Different subset sizes of principal feature vectors are provided to five different decision models. The classification models include the J48 decision tree, k-nearest neighbour (kNN), random forest (RF), and least-squares support vector machine (LS-SVM) with polynomial and radial basis kernels. Results: The RF-based classifier outperformed among all compared decision models and achieved an average accuracy of 96% with 4% standard deviation, and an area under the receiver operating characteristic (ROC) curve of 99%. LS-SVM (RBF) also shows promising results (i.e., 89% accuracy) when the least number of principal features was used. Furthermore, the performance of each classifier on different subset sizes of principal features was (80%–96%) for most performance metrics. Conclusion: The presented medical decision support system demonstrates the potential proof for accurate multi-class classification of brain abnormalities; therefore, it has a potential to use as a diagnostic tool for the medical practitioners. View Full-Text
Keywords: computer aided diagnostic system; neuroimaging; brain magnetic resonance imaging (MRI); multi-classification; medical imaging computer aided diagnostic system; neuroimaging; brain magnetic resonance imaging (MRI); multi-classification; medical imaging

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Siddiqui, M.F.; Mujtaba, G.; Reza, A.W.; Shuib, L. Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System. Symmetry 2017, 9, 37.

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