Comparative Analysis of ML Models for Brain Tumor Detection

Brain tumor identification is crucial in the diagnosis and treatment of brain-related illnesses. With the introduction of machine learning (ML) techniques, great progress has been made in automating the detection process, resulting in better accuracy and efficiency. In this study, we explore and evaluate the performance of various machine learning models for brain tumor diagnosis. We investigate the possibility of several ML approaches, such as support vector machines(SVM), random forests, neural networks, and logistic regression, in reliably diagnosing brain tumors from medical imaging data. We examine the performance parameters such as area under the curve (AUC), classification accuracy (CA), F1, precision, recall, and Matthews correlation coefficient (MCC) for each model by rigorous examination utilizing a varied dataset.