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Automatic Brain MRI Classification Using Modified Ant Colony System and Neural Network Classifier

In this paper, a hybrid intelligent machine learning technique for automatic classification of brain magnetic resonance images is presented. The proposed multistage technique involves the following computational methods, Otsu's method for skull removal, Fuzzy Inference System for image enhancement, Modified Fuzzy C Means with the Optimized Ant Colony System for image segmentation, Second Order Statistical Analysis and Wavelet Transform Method for feature extraction and the Feed Forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 200 images consisting of 100 normal and 100 abnormal (malignant and benign tumors) from a real human brain MRI data set. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs a least number of features for classification.