A Comprehensive Exploration of U-Net, 3D U-Net, and Capsule Networks for Brain Tumor Segmentation in 3D Medical Imaging

U-Net specializes in precise image segmentation, 3D U-Net facilitates volumetric analysis for comprehensive tumor assessment, while Capsule Networks contribute to interpretability and feature extraction, enhancing diagnostic insights. The ensemble model, comprising multiple base models, is trained on a diverse and comprehensive dataset and evaluated using hold-out validation to determine its generalization capability. Moreover, ensemble learning techniques such as model averaging are explored to effectively combine the predictions of individual models, thereby integrating collective intelligence for enhanced disease detection. The proposed integrated approach demonstrates notable advancements in diagnostic accuracy and reliability, promising more precise and dependable detection of brain tumors. The findings of this study hold significant implications for the medical image analysis, offering a pathway towards improved patient care through the adoption of state-of-the-art disease detection methodologies. By leveraging the synergistic capabilities of deep learning architectures, this research contributes towards advancing the frontiers of medical imaging and also the diagnostic capabilities in neuro-oncology.