A professional estimate on the computed tomography brain tumor images using SVM-SMO for classification and MRG-GWO for segmentation

In many applications of image processing and computer vision, it is image segmentation that is widely used. The study of the Computed Tomography (CT) images considers image segmentation a very important and vital part in identifying the different kinds of tumor. The classification of the tumor and the non-tumor images followed by the segmentation of tumor region in CT images is done by the proposed methodology. The process of classifying is carried out by Support Vector Machine (SVM) with different kernel functions and optimization techniques. This SVM classifier with Sequential Minimal Optimization (SMO) is predominant over the other methodologies in the analysis of classification process. The segmentation process after the classification process is performed by the Modified Region Growing (MRG) with threshold optimization. As regards the threshold optimization, certain algorithms such as Harmony Search (HS), Evolutionary Programming (EP) and Grey Wolf Optimization (GWO) are made use of. It is with the aid of a wide set of performance measures that the results are demonstrated. The comparative analysis in terms of sensitivity, specificity and accuracy is done for the optimization techniques said supra. An accuracy rate of 99.05% in the analysis of segmentation process is obtained using the GWO technique. It is in the working platform of MATLAB that this proposed methodology is implemented. The experimental results obtained depict that the proposed methodology (MRG-GWO) enjoys high accuracy with enhanced tumor detection in total contrast to the other two techniques (HS and EP) in comparison.