Journal :   Research Journal of Pharmacy and Technology

Volume No. :   10

Issue No. :  3

Year :  2017

Pages :   715-720

ISSN Print :  0974-3618

ISSN Online :  0974-360X


Allready Registrered
Click to Login

Detection and Classification of MRI Brain Images For Head/Brain Injury Using Soft Computing Techniques

Address:   Shrikant Burje1*, Prof. Dr. Sourabh Rungta1, Prof. Dr. Anupam Shukla2
1Rungta College of Engineering and Technology, Bhilai, India
2IIITM Gwalior, M.P, INDIA
*Corresponding Author
DOI No: 10.5958/0974-360X.2017.00134.2

It is essential to have a rigorous computerized system for Magnetic Resonance Images (MRI) of the brain for medical perception and clinical analysis. This article focuses on our proposed method of hybrid approach for classification of normal and abnormalities in magnetic resonance brain images. Wavelet and PCA were functioning feature extraction and reduction from image respectively. The featured images finally were linked to Neuro-Fuzzy Classifier (NFC) for classification. The proposed methodology, including three basic steps, namely preprocessing, training and classified output. It extracts and reduced the dimension of features from the set of scan brain MR images of patients. Once preprocessing done, the featured image trained by soft computing based fuzzy neural tool and finally fed to the Neuro-Fuzzy Classifier (NFC) for detection of abnormalities in new MR images. The Hybrid NFC is combined with K- fold fuzzy C-means Neuro-Fuzzy Classifier which is used to enhance abstraction of NFC. We focus on common brain diseases such as meningioma, Alzheimer's and visual agnosia as an abnormal brain. K-Fold Neuro Fuzzy Classifier provides the accuracy around 98% with minimum computational time.
Shrikant Burje, Sourabh Rungta, Anupam Shukla. Detection and Classification of MRI Brain Images For Head/Brain Injury Using Soft Computing Techniques. Research J. Pharm. and Tech. 2017; 10(3): 715-720.
[View HTML]      [View PDF]

Visitor's No. :   362957