Deep LearnIng and Machine Learning for Brain Tumor Detection: A Review, Challenges, and Future Directions

Abstract Brain tumor diagnosis and classification remain critical challenges in modern healthcare. Recent advancements in artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), have significantly enhanced medical image analysis, enabling automated and accurate detection of brain tumors. This paper reviews a wide range of ML and DL approaches for brain tumor detection, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), hybrid architectures, vision transformers (ViTs), transfer learning (TL), attention mechanisms, and ensemble learning methods. Also, AI applications are highlighted across different medical imaging modalities, summarize commonly used publicly available datasets, and discuss preprocessing techniques adopted in recent studies. Furthermore, the paper addresses evaluation metrics, compares state-of-the-art DL approaches, and examines key challenges and limitations. Finally, future research directions are proposed to guide the development of more robust and clinically effective AI-based solutions. This review aims to provide researchers and clinicians with a comprehensive understanding of AIs potential in advancing brain tumor diagnosis and detection.