MRI-based brain tumor detection and types of classification using CEHJB-RI and JMST

Abstract

The Brain Tumor (BT), which forms in the brain cells and spreads to the whole brain, may lead to death. Hence, early diagnosis of BT is significant. Still, the detection of BT between the skull and brain region is not concentrated, which results in misclassification outcomes. Thus, this article proposes Magnetic Resonance Imaging (MRI)-based BT detection and types’ classification utilizing Carlitz Exponential Hamilton Jacobi Bellman-based Reinforcement Learning (CEHJB-RL) and JenSorensen similarity-based Minimum Spanning Tree (JMST). Primarily, raw MRI images are taken and then pre-processed. Then, with skull and without skull regions are extracted from the pre-processed image and are subjected to the graph construction. Conversely, the edges are detected from the pre-processed image that can be patch-extracted and subjected to graph construction. By utilizing JMST and Morphological Operations (MO), the graphs are constructed. Thereafter, the features are extracted and fed to the classifier. Then, the type of BT is classified by the classifier using CEHJB-RL. Concerning the performance metrics, the outcomes illustrated that the proposed technique attained a higher accuracy (99.27%), which is better than other existing techniques.