Brain tumor segmentation and classification using image processing

Abstract

In this study, we propose an automated approach for brain tumor segmentation and classification from MRI images which is a leading sector in medical imaging research because of the importance they have for patient diagnosis and therapy. This project does a comprehensive literature review and provides approaches to improve the accuracy and efficiency of brain tumor detection. The dataset, sourced from Kaggle, consists of 4237 MRI images categorized into four classes: No Tumor, Glioma, Meningioma, and Pituitary tumors. Corresponding segmentation masks are also included for precise delineation of tumor regions. The detection system's performance is improved through the use of techniques such as feature extraction, image resizing, and grayscale conversion. Several segmentation and classification methods, including Convolutional Neural Networks (CNNs), Artificial Neural Network (ANN), Graph Neural Network (GNN) are evaluated. The segmentation task is performed using a customized CNN, achieving an impressive accuracy of 96.32%. For the classification task, a variety of machine learning models were tested, including Random Forest, Extra Trees, Logistic Regression, Gradient Boosting, Support Vector Machine, Decision Tree, Naive Bayes, ANN, and GNN. Among these, the Graph Neural Network (GNN) demonstrated superior performance, achieving an accuracy of 99.65%. The project also involves developing a web application where users can upload MRI images to receive automatic tumor detection and classification. This application aims to assist medical professionals by providing quick and accurate diagnoses, potentially improving patient outcomes. The project aims to overcome existing obstacles in brain tumor identification while also contributing to the development of accurate and trustworthy diagnostic tools for healthcare professionals.