Work place: Department of Instrumentation and Control Engineering, Gautam Buddh Technical University JSS Academy of Technical Education, Noida (U.P), India
Research Interests: Medical Image Computing, Image Processing, Computer systems and computational processes, Medical Informatics
Sumitra received Bachelor of Engineering degree in Electronics and Instrumentation in the year 2002 from Madurai Kamaraj University, Tamilnadu, India. She is currently working towards her Masters Degree in Control and Instrumentation at Rajasthan Technical University, Kota, India. Presently, she is working as Assistant Professor in the Department of Instrumentation and Control in JSS Academy of Technical Education, Noida, India. She has presented many research papers in International Conferences and her research interests include Image Processing, Industrial Instrumentation, Medical Electronics, Transducers and Sensors etc.
DOI: https://doi.org/10.5815/ijigsp.2013.02.07, Pub. Date: 8 Feb. 2013
The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Hence, this paper presents Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of the following stages namely, feature extraction, dimensionality reduction, and classification. The features extracted from the magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features such as mean, median, variance, correlation, values of maximum and minimum intensity. In the classification stage, classifier based on Back-Propagation Neural Network has been developed. This classifier has been used to classify subjects as normal, benign and malignant brain tumor images. The results show that BPN classifier gives fast and accurate classification than the other neural networks and can be effectively used for classifying brain tumor with high level of accuracy.[...] Read more.
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