A Survey on an Effective Identification and Analysis for Brain Tumour Diagnosis using Machine Learning Technique

Abstract

The hottest issue in medicine is image analysis. It has drawn a lot of researchers since it can effectively assess the severity of the condition and forecast the outcome. The noise trimming outcomes, on the other hand, have reduced with more complex trained images, which has tended to result in a lower prediction exactness score. So, a novel Machine Learning prediction framework has been built in this present study. This work also tries to predict brain tumours and evaluate their severity using MRI brain scans. Using the boosting function, the best results for error pruning are produced. The Proposed Solution function was then used to successfully complete the feature analysis and tumour prediction operations. The intended framework is evaluated in the Python environment, and a comparative analysis is performed to examine the prediction improvement score. It was discovered that an original MLPM model had the best tumour prediction precision.