Comprehensive Analysis of Machine Learning and Deep Learning Algorithms for Bt Classification

An abnormal growth of cells in the brain is named as a BT (BT), which can either be benign or malignant, and affects brain function by exerting pressure on surrounding tissues, thereby disrupting normal activities. In medical imaging, BT segmentation and classification are critical tasks that play an important role in early diagnosis. In recent years, Machine Learning (ML) and Deep Learning (DL) methods have significantly advanced the analysis of MRI images with high reliability and accuracy. This paper provides an overview of various ML and DL algorithms used for BT identification, focusing on their segmentation and classification capabilities. It effectively discusses the processes of pre-processing, feature extraction, tumor region segmentation, and classification using models such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). The performance of these models is evaluated using metrics such as accuracy, recall, precision, F1-score, sensitivity, and specificity. The survey also explores the advantages, limitations, and comparative analysis of existing techniques. In this research a reference for aiming to develop efficient, intelligent, and accurate BT classification systems.