Comparative Study Between Brain Tumor Segmentation, Localization and Visualization Techniques

This paper provides a detailed comparative analysis of advanced techniques for the segmentation, localization, and visualization of brain tumors, specifically focusing on meningiomas, one of the most common intracranial tumors. Accurate diagnosis and treatment planning for these tumors are vital for better patient outcomes. The study reviews various state-of-the-art methodologies, including deep learning techniques like convolutional neural networks (CNNs), which have significantly improved tumor analysis accuracy and efficiency. The research highlights several segmentation strategies that utilize multi-modal imaging data and transfer learning to enhance diagnostic performance. It also explores innovative localization methods employing nonlinear correlation learning to predict tumor recurrence, along with dual frameworks that integrate image-based and feature-based classification. Additionally, the paper discusses visualization techniques that offer valuable insights into tumor characteristics such as adhesion and enhancement patterns, which are crucial for surgical planning. Despite the positive outcomes demonstrated by these methods, the paper acknowledges challenges, including limited datasets and the necessity for better generalizability in various clinical contexts.