Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation

Brain tumors, whether cancerous or noncancerous, can be life-threatening due to abnormal cell growth, potentially causing organ dysfunction and mortality in adults. Brain tumor segmentation (BTS) and brain tumor classification (BTC) technologies are crucial in diagnosing and treating brain tumors. They assist doctors in locating and measuring tumors and developing treatment and rehabilitation strategies. Despite their importance in the medical field, BTC and BTS remain challenging. This comprehensive review specifically analyses machine and deep learning methodologies, including convolutional neural networks (CNN), transfer learning (TL), and hybrid models for BTS and BTC. We discuss CNN architectures like U-Net++, which is known for its high segmentation accuracy in 2D and 3D medical images. Additionally, transfer learning utilises pre-trained models such as ResNet, Inception, etc., from ImageNet, fine-tuned on brain tumor-specific datasets to enhance classification performance and sensitivity despite limited medical data. Hybrid models combine deep learning techniques with machine learning, using CNN for initial segmentation and traditional classification methods, improving accuracy. We discuss commonly used benchmark datasets in brain tumors research, including the BraTS dataset and the TCIA database, and evaluate performance metrics, such as the F1-score, accuracy, sensitivity, specificity, and the dice coefficient, emphasising their significance and standard thresholds in brain tumors analysis. The review addresses current machine learning (ML) and deep learning (DL) based BTS and BTC challenges and proposes solutions such as explainable deep learning models and multi-task learning frameworks. These insights aim to guide future advancements in fostering the development of accurate and efficient tools for improved patient care in brain tumors analysis.