ARCHIVES

Since brain tumors have a complicated morphology, subtle limits, and a variety of characteristics, diagnosing them is a crucial and difficult task in medical imaging. The detection, classification, and segmentation of brain tumors have been profoundly altered by recent developments in machine learning (ML), especially deep learning (DL). This review paper provides a thorough analysis of the most recent advances in ML methods used in brain tumor diagnosis, emphasizing significant advancements in hybrid models, recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transfer learning techniques. The study classifies current methods according to clinical usefulness, learning paradigms (supervised, unsupervised, and semi-supervised), and imaging modalities. It also examines the correctness, accessibility, robustness, and applicability of existing approaches, highlighting their advantages and disadvantages. Challenges such as data scarcity, class imbalance, and model transparency are discussed alongside recent trends like explainable AI. Finally, the paper outlines future research directions aimed at improving diagnostic reliability, real-time deployment, and clinical translation of ML-based brain tumor analysis systems.