IDENTIFYING BRAIN TUMOUR FROM MRI IMAGE USING MODIFIED FCM AND SUPPORT VECTOR MACHINE

Brain tumor detection in magnetic resonance images (MRI) is essential in medical diagnosis because it provides information associated to anatomical structures as well as potential abnormal tissues necessary to treatment planning and patient follow-up. This paper proposes an intelligent segmentation technique to identify normal and abnormal slices of brain MRI data. It consists of four steps which includes i) Preprocessing ii) segmentation using Modified fuzzy C-means algorithm iii) Feature extraction of the region like mean, standard deviation, range and pixel orientation and iv) Final classification using the support vector machine. The performance of the proposed technique is systematically evaluated using the MRI brain images received from the public sources. For validating the effectiveness of the modified fuzzy c-means, we have considered the quantity rate parameter. For the evaluation of the proposed technique of tumor detection, we make use of sensitivity, specificity and accuracy values which we compute from finding out false positive, false negative, true positive and true negative. Comparative analyses were done by the normal and modified FCM using both the Neural Network (FFNN) and SVM. From the results obtained, we could see that the proposed technique achieved the accuracy of 93% for the testing dataset, which clearly demonstrated the effectiveness of the modified FCM when compared to the normal technique.