Abstract
Brain tumors pose a significant health challenge, necessitating the development of reliable and automated detection methods within the medical field. Swift and accurate identification of these tumors is crucial for effective treatment and patient well-being. These abnormal growths arise from uncontrolled cell proliferation, depriving healthy brain tissue of vital nutrients and leading to organ dysfunction. Currently, the conventional approach involves the manual examination of brain MRI scans by medical professionals. However, this method is hindered by the diverse shapes and sizes of tumors, often resulting in time-consuming and occasionally imprecise evaluations. The advent of automation presents immense potential, enhancing efficiency and allowing medical practitioners to dedicate more time to direct patient care. Traditional machine learning approaches have historically relied on labor-intensive feature engineering. In this research, we propose an innovative approach that integrates the U-Net model, a Convolutional Neural Network (CNN), and a Self-Organizing Feature Map (SOFM) in an ensemble technique for precise brain tumor segmentation using the BRATS 2020 dataset. Our evaluation not only focuses on segmentation accuracy but also leverages survival data from the dataset to predict patient survival rates.
Index Terms—Automated brain tumor detection, BRATS 2020 dataset, U-Net, Convolutional Neural Network (CNN), Modified Self-Organizing Feature Map (MSOFM), survival analysis, tumor identification.


Objective
The primary objective of this research is to develop an advanced ensemble-based technique for accurate brain tumor segmentation and patient survival prediction using deep learning methodologies. The study aims to integrate U-Net, Convolutional Neural Networks (CNNs), and a Self-Organizing Feature Map (SOFM) to enhance the precision and robustness of tumor detection. By leveraging the BRATS 2020 dataset, the proposed approach seeks to achieve the following specific goals:
1. Automated Brain Tumor Segmentation – Develop a deep learning-driven segmentation model to accurately delineate tumor regions from brain MRI scans, overcoming the limitations of manual assessment and conventional machine learning techniques.
2. Integration of an Ensemble Approach – Combine the strengths of U-Net for segmentation, CNNs for feature extraction, and SOFM for improved classification to enhance segmentation accuracy and adaptability to diverse tumor morphologies.
3. Survival Prediction using Clinical Data – Utilize survival-related information from the BRATS 2020 dataset to predict patient survival rates based on tumor characteristics, enabling early prognosis and personalized treatment planning.
4. Performance Evaluation – Assess the model's effectiveness using metrics such as accuracy segmentation, as well as survival prediction accuracy to validate clinical applicability.
5. Enhancing Medical Decision Support – Provide an AI-powered solution that can assist radiologists and oncologists in making faster, more reliable diagnoses and survival predictions, thereby improving patient outcomes.