Multi class disorder detection of magnetic resonance brain images using composite features and neural network

• Published in: Biomedical engineering letters 2019, 9(2), , pp.221-231
• Main Authors: Kale, Vandana V., Hamde, Satish T., Holambe, Raghunath S.
• Format: Article
• Language: English
• Subjects: Abnormalities; Artificial neural networks; Back propagation networks; Bayesian analysis; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Brain; Classification; Diagnosis; Disorders; Engineering; Experimentation; Feature recognition; Image classification; Image detection; Magnetic resonance; Magnetic resonance imaging; Medical and Radiation Physics; Medical diagnosis; Medical imaging; Neural networks; Object recognition; Original; Original Article; Physicians; Therapeutic applications; Wavelet; 의공학
• ISSN: 2093-9868; 2093-985X
• DOI: 10.1007/s13534-019-00103-1

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 classified using back propagation neural network. Bayesian regulation is adopted to find 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, fivefold stratified cross validation technique is used. The proposed system yields multi class disease classification accuracy of 100% in differentiating 90 MR brain images into 18 classes and 97.81% in differentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifier create a competent and reliable system for the identification 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 classification performance results.