Biopsy for brain tumor assessment is invasive and risky, motivating noninvasive alternatives based on MRI. This review synthesizes recent advances toward an automated, robust, and intelligent system for early brain tumor diagnosis and grading. A six-step pipeline is presented that integrates standardized MRI preprocessing with histogram equalization, adaptive gamma correction, and Wiener filtering; metaheuristic optimization using Particle Swarm Optimization, sine–cosine, and Grey Wolf Optimization for parameter and feature selection; volumetric segmentation through 3D U- Net and related architectures; and classification through deep learning, transfer learning, and complementary machine learning models. Privacy-preserving deployment within smart healthcare ecosystems is further highlighted using federated learning and secure optimization. Evidence across recent studies indicates that the combination of principled enhancement, optimization, and modern segmentation/classification markedly improves detection, boundary delineation, and grading robustness. Concluding with challenges in cross-site generalization, rigorous clinical validation, and trustworthy AI, the discussion outlines a path to clinically viable, noninvasive MRI-based tumor diagnostics.
Tanuja et al. (Wed,) studied this question.