Abstract

Automated and accurate classification of magnetic resonance images (MRIs) of the brain has great importance for medical analysis and interpretation. This paper presents a hybrid optimized classification method to classify the brain tumor by classifying the given magnetic resonance brain image as normal or abnormal. The proposed system implements a gray wolf optimizer (GWO) combined with a supervised artificial neural network (ANN) classifier to achieve enhanced MRI classification accuracy via selecting the optimal parameters of ANN. The introduced GWO–ANN classification system performance is compared to the traditional neural network (NN) classifier using receiver operating characteristic analysis. Experimental results obviously indicate that the presented system achieves a high classification rate and performs much better than the traditional NN classifier.

© 2017 Optical Society of America

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