Machine Learning on the Diagnosis of Brain Tumors

Brain tumors are an uncontrolled proliferation of abnormal cells in the brain. They are classified according to world health organization (WHO) based on: 1) their originating cell as astrocytoma meningioma oligodendrocytoma, 2) from grade I to grade IV to classify benign and malignant tumor types. Benign tumors with low grade I and II, and malignant tumors with high grade III and IV, 3) the number of cells in mitosis and 4) their rate of proliferation as well as the presence of area of necrosis. In this investigation, we will focus on evaluating the performance of machine learning models in detecting abnormal class of brain tumor. Our aims is to use a computer aided technology to perform the diagnostic of pathology on the brain tumor based on the K-Nearest Neighbor (KNN) classifier, and compare the obtained results with the anatomopathologist diagnosis. This study will serve as a guideline that tells us how much better a model is then making correct prediction. Several patients having a brain tumor have been diagnosed to assess the performance metrics of the proposed algorithm model. To conclude, this method works better indicating a pretty good prediction and high recognition rate in detecting a patient disease effectively which results in reducing the heavy physician workloads and diagnostic time.