Performance Evaluation of Machine Learning Classification of Brain Tumors with WEKA and Python Programming

The manual interpretation of brain tumors MRI scans by medical personnel can be time-consuming and require a significant amount of manpower. This highlights the need for more efficient and accurate methods of interpreting MRI scans, such as machine learning implementations. By developing and implementing machine learning models capable of interpreting MRI scans, we can improve the speed and accuracy of diagnosis for brain tumors, and provide better patient outcomes. Therefore, the performance evaluation of both WEKA and Python as it correlates to image classification with the aid of appropriate filters and classifiers is necessary for the advancement of medical imaging. The objectives of the chapter are to: (i) evaluate the performance of machine learning classification of brain tumors with WEKA and PYTHON programming using MRI scan images (ii) deploy the best model for the brain tumor classification in WEKA and PYTHON programming.