Advancements in Brain Tumor Detection using Machine Learning Applications from MRI Image Analysis

The present research uses a dataset of 2034 images to conduct a detailed evaluation of machine learning models for the purpose of identifying brain tumours in MRI scans. Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), and Logistic Regression (LR) were four alternative models that were thoroughly examined based on their performance indicators. The Convolutional Neural Network was shown to be the most effective model, with a high accuracy of 97.5%, great precision, recall, and F1 scores. This demonstrates how deep learning, namely CNNs, can be used to automate and improve brain tumour identification in medical imaging. The study also underlines the SVM and RF models' durability and adaptability, which demonstrated exceptional performance metrics and were thus acceptable for use in real-world healthcare applications. Despite having substantially lower scores, the Logistic Regression model also significantly aids in diagnosis. Finally, this study demonstrates the importance of machine learning in the medical business and the potential for early brain tumour detection, which would improve patient care and treatment outcomes. It emphasises how the field of medical image analysis is always evolving and improving, meaning advances in the detection and management of major medical disorders.