Abstract
Substantial clinical statistics along with analytical features can be extracted from brain tumor images. The identified quantitative measures of exact tumor regions aid physicians and radiologists in effective treatment. Magnetic Resonance Imaging (MRI) images of the brain are considered. Handling these images is challenging mainly owing to variance in addition to complexity in detecting tumors. In this paper, images are segmented using Enhanced Possibilistic C-Means Algorithm (EPCMA) and classified using Multi-Layered Extreme Learning Machine (ML-ELM). System performance is observed based on classification accuracy, sensitivity and specificity. Index Terms: Brain Tumor, Possibilistic C-Means Algorithm, Extreme Learning Machine, MRI
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