Brain Tumor Classification using Machine Learning Approach

Brain tumors are a major worldwide health problem, with early and correct diagnosis critical for successful treatment and better patient outcomes. Advances in medical imaging and machine learning have created new opportunities for automated diagnostic tools to help doctors identify and categorize brain cancers with greater accuracy. This study investigates the use of machine learning techniques, notably Random Forest classifiers and 2D Convolutional Neural Networks, to classify brain tumors using MRI data. The models were trained and evaluated using a publicly available Kaggle dataset, which included a wide range of brain MRI images labeled with tumor classifications. While the Random Forest model was simple and computationally efficient, the 2D CNN surpassed it in terms of accuracy and precision, demonstrating the power of deep learning for dealing with complicated imaging data. These findings emphasize the importance of using machine learning in medical diagnostics, demonstrating how sophisticated algorithms may increase diagnostic accuracy while also providing significant insights to guide treatment decision-making. Integrating such technologies may improve early detection, streamline procedures, and eventually lead to better patient care in cancer.