Classification of Brain MRI Images using Multi Kernel Support Vector Machines

The research of clinical image processing gathers its essentialities with its growth in the requirement of efficient and automated diagnosis in minimal duration of time. Apart from that the retrieval system of clinical images has to be provided as the tool for clinicians and radiologists for acquiring the clinical images as that of the fault image present in the content. Magnetic Resonance Imaging (MRI) is an emerging technique in the field of medical image acquisition and processing that has played an essential and vital role in the research field of neuro science in order to study the images of brain. Classifying the brain images to different clinical state is very important task for clinicians to make decisions for proceeding with the treatment to cure major deadly disorders. Hence it is considered as an essential sector in the retrieval system for distinguishing the normal subjects from the abnormal one. Brain tumor is considered as one of the most important conditions of abnormalities which results in death at most of the cases. In this proposed study, the brain images are taken as a cluster of databases with different clinical conditions in both normal and abnormal state with which 80% of data are utilized for training the classifier and remaining 20% of data are being used as test data. The sequence of process that are used for detecting the clinical state of MRI images are followed by preprocessing, segmentation, feature extraction, feature selection and classification. The multi kernel Support Vector Machine using Radial Basis Function is being used for classifying the MRI images. The performance metrics such as sensitivity, specificity and accuracy are being determined and it is realized to be as an efficient strategy that is useful for categorizing the clinical state of brain whether it is a normal one or abnormal one. It is being inferred that the classification accuracy is being attained with 98.62% in predicting the clinical normality and abnormality conditions.