Brain Tumor Semantic Segmentation and Classification using Deep Learning techniques

Abstract

ABSTRACT : Brain cancer, speciőcally Glioma, is a devastating disease with a very low chance of survival. In fact, only 3.6% of patients diagnosed with high-grade Glioma survive beyond őve years. Magnetic resonance imaging (MRI) is commonly used to examine brain tumors in clinical practice. Fortunately, deep learning methods have shown remarkable potential in effectively segmenting brain tumors and have yielded promising results in various biomedical applications. This project aims to develop and implement a deep learning model capable of performing semantic segmentation of brain tumors. The proposed model leverages advanced deep learning techniques to accurately segment tumor regions from medical imaging data The project also includes the development of an online platform that provides a user-friendly interface for monitoring and diagnosis of patients with brain tumors. The online platform will allow users to easily upload their medical images, which will then undergo the segmentation and classiőcation process. The results will be displayed to the users, providing them with valuable insights into the tumor characteristics and aiding in medical decision-making.