numaanfarooq/Brain_Tumour_Segmentation_and_Survival_Prediction_Using_Deep_Learning

This project aims to create a deep learning based model for the segmentation of brain tumours and their subregions from MRI scans, as well as the prediction of patient survival . The segmentation is performed using a U-Net architecture, while survival prediction is done using CNN models.

Brain Tumor Segmentation and Survival Prediction:-

Project Overview

The Brain Tumor Segmentation and Survival Prediction project is designed to leverage advanced deep learning techniques to automate the segmentation of brain tumors from MRI scans and to predict patient survival rates.

This project employs a U-Net architecture for precise tumor segmentation, enabling clinicians to visualize and analyze tumor boundaries more efficiently. Following segmentation, the project extracts relevant radiomic features to facilitate the prediction of survival outcomes, utilizing various machine learning algorithms.

The integration of these components aims to improve diagnostic accuracy, enhance treatment planning, and ultimately contribute to better patient outcomes in neuro-oncology.

Key Objectives: Automate Tumor Segmentation: Utilize state-of-the-art deep learning models to accurately delineate brain tumors from MRI images. Predict Patient Survival: Analyze extracted features to forecast survival rates, assisting healthcare professionals in clinical decision-making. Enhance Visualization: Provide clear visual outputs of segmented tumors, making the results intuitive and accessible.