Comparing Machine Learning Models to Determine Which is Most Effective at Detecting Brain Tumors

In this project, by using machine learning techniques to analyze various brain tumor scans, the goal was determine which are most efficient and accurate in determining presence of tumors. Specifically, adequately process a dataset if tumors can be detected with more than 75% accuracy learning. The chosen includes just over 250 axial MRI thus providing sufficient properly analyze. This research is applicable healthcare field, specifically relating work done neurologists radiologists. If way detect not determined soon, individuals may have continue waiting longer necessary for their results. K-Nearest Neighbors, Decision Trees, Muti-Layer Perceptron Models were compared algorithm effective at an scan.