Multi class disorder detection of magnetic resonance brain images using composite features and neural network
10.1007/s13534-019-00103-1
- Author:
Vandana V KALE
1
;
Satish T HAMDE
;
Raghunath S HOLAMBE
Author Information
1. Department of Instrumentation Engineering, AISSMS's Institute of Information Technology, Pune, Maharashtra 411001, India. vandanav_kale@yahoo.co.in
- Publication Type:Original Article
- Keywords:
Magnetic resonance imaging;
Discrete wavelet transform;
Gray level co-occurrence matrix;
Back propagation neural network;
Multi class classifi cation
- MeSH:
Brain Diseases;
Brain;
Dataset;
Diagnosis;
Generalization (Psychology);
Magnetic Resonance Imaging;
Schools, Medical;
Weights and Measures
- From:
Biomedical Engineering Letters
2019;9(2):221-231
- CountryRepublic of Korea
- Language:English
-
Abstract:
Brain disorder recognition has becoming a promising area of study. In reality, some disorders share similar features and signs, making the task of diagnosis and treatment challenging. This paper presents a rigorous and robust computer aided diagnosis system for the detection of multiple brain abnormalities which can assist physicians in the diagnosis and treatment of brain diseases. In this system, we used energy of wavelet sub bands, textural features of gray level co-occurrence matrix and intensity feature of MR brain images. These features are ranked using Wilcoxon test. The composite features are classifi ed using back propagation neural network. Bayesian regulation is adopted to fi nd the optimal weights of neural network. The experimentation is carried out on datasets DS-90 and DS-310 of Harvard Medical School. To enhance the generalization capability of the network, fi vefold stratifi ed cross validation technique is used. The proposed system yields multi class disease classifi cation accuracy of 100% in diff erentiating 90 MR brain images into 18 classes and 97.81% in diff erentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifi er create a competent and reliable system for the identifi cation of multiple brain disorders which can be used in clinical applications. The Wilcoxon test outcome demonstrates that standard deviation feature along with energies of approximate and vertical sub bands of level 7 contribute the most in achieving enhanced multi class classifi cation performance results.
