<![CDATA[Rapid advancements in computer vision and machine learning have significantly revolutionized medical imaging one such application is brain tumor detection and classification. Deep learning has emerged as a powerful tool, which offers exceptional capabilities in handling complex medical datasets. However, the current systems still face challenges in achieving optimal accuracy, robustness and clinical interpretability. This study presents a comprehensive survey of brain tumor segmentation, classification and detection techniques using deep learning, metaheuristic and hybrid approaches. The detailed quantitative evaluations of conventional and emerging methods are conducted by examining key performance metrics, dataset characteristics, strengths, and limitations. This review highlights recent breakthroughs by analyzing state-of-the-art techniques from the past five years, research gaps and potential directions for future advancements. These findings provide insights into novel architectures, optimization strategies and clinical applications which ultimately guide researchers towards more robust, interpretable and clinically impactful artificial intelligence (AI)-driven solutions for brain tumor analysis.]]>
Copyrights © 2026
