Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

The Brain Tumor (BT) is created by an uncontrollable rise of anomalous cells in brain tissue, and it consists 2 types cancers they are malignant benign tumors. benevolent BT does not affect the neighbouring healthy normal tissue; however, could adjacent tissues, which results death. Initial recognition highly significant to protecting patient’s life. Generally, can be identified through magnetic resonance imaging (MRI) scanning technique. But radiotherapists offering effective tumor segmentation MRI images because position unequal shape brain. Recently, ML has prevailed against standard image processing techniques. Several studies denote superiority machine learning (ML) techniques over Therefore, this study develops novel detection classification model using met heuristic optimization with (BTDC-MOML) model. To accomplish effectively, a Computer-Aided Design (CAD) Machine Learning technique proposed research manuscript. Initially, input pre-processing performed Gaborfiltering (GF) based noise removal, contrast enhancement, skull stripping. Next, mayfly Kapur’s thresholding process takes place. For feature extraction proposes, local diagonal extreme patterns (LDEP) exploited. At last, Extreme Gradient Boosting (XGBoost) used for process. accuracy analysis terms accuracy, validation determine efficiency work. experimental demonstrates its promising performance other existing methods.