Brain Tumor Classification From MRI Images Using Machine Learning

TL;DR

This paper presents a machine learning-based system for early detection and classification of brain tumors from MRI images, emphasizing the importance of feature extraction and the use of ensemble methods for improved accuracy.

Contribution

It introduces a novel ensemble machine learning approach for brain tumor classification from MRI images, leveraging advanced feature extraction techniques.

Findings

Deep learning improves classification accuracy

Ensemble methods outperform individual classifiers

Early detection enhances treatment planning

Abstract

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).