Abstract
Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death. Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue. Radiologists checked the affected tissue in the slice-by-slice manner, which was time-consuming and hectic task. Therefore, auto segmentation of the affected part is needed to facilitate radiologists. Therefore, we have considered a hybrid model that inherits the convolutional neural network (CNN) properties to the support vector machine (SVM) for the auto-segmented brain tumor region. The CNN model is initially used to detect brain tumors, while SVM is integrated to segment the tumor region correctly. The proposed method was evaluated on a publicly available BraTS2020 dataset. The statistical parameters used in this work for the mathematical measures are precision, accuracy, specificity, sensitivity, and dice coefficient. Overall, our method achieved an accuracy value of 0.98, which is most prominent than existing techniques. Moreover, the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.