  Brain tumor is a life-threatening problem and hampers the normal functioning
of the human body. The average five-year relative survival rate for malignant
brain tumors is 35.6 percent. For proper diagnosis and efficient treatment
planning, it is necessary to detect the brain tumor in early stages. Due to
advancement in medical imaging technology, the brain images are taken in
different modalities. The ability to extract relevant characteristics from
magnetic resonance imaging (MRI) scans is a crucial step for brain tumor
classifiers. Several studies have proposed various strategies to extract
relevant features from different modalities of MRI to predict the growth of
abnormal tumors. Most techniques used conventional methods of image processing
for feature extraction and machine learning for classification. More recently,
the use of deep learning algorithms in medical imaging has resulted in
significant improvements in the classification and diagnosis of brain tumors.
Since tumors are located at different regions of the brain, localizing the
tumor and classifying it to a particular category is a challenging task. The
objective of this project is to develop a predictive system for brain tumor
detection using machine learning(ensembling).
