Ensemble Machine Learning Approach for Brain Tumor Classification Analysis

Diagnosis of Cancer has become very common these days, and brain tumor is diagnosed second commonly after breast cancer. Classification of brain tumors is an important outcome in identifying the presence of tumors and deciding on treatment. Due to its low survival rate, the importance of proper classification is required. To solve this problem, CAD model is used to find the easiest way, and machine learning algorithms are used with incredible accuracy to get optimistic results. Recent advances in building better models have received attention and various deep learning algorithms have been used. In this work, several classification algorithms were implemented to achieve the best results. MRI images of the brain are considered more important than CT images. This dataset contains about 300 MRI images along with the dataset from Cancer Archives and is divided into abnormal and normal classes to obtain binary classification results. In the initial stage, the preprocessing method is used, and in the later final stage, the classification algorithm is used. To improve accuracy, an ensemble of different classifiers is used with a set of respective input data. Thus, the proposed model proved to provide more precise accuracy than the previous classifier models used in the existing method.