AI in MRI brain tumor diagnosis: A systematic review of machine learning and deep learning advances (2010–2025)

Abstract

Brain tumors present critical health challenges due to abnormal tissue growth within the brain, potentially leading to life-threatening conditions if left untreated. MRI stands as the primary diagnostic tool for identifying brain tumors, offering superior resolution and tissue differentiation compared to other imaging modalities. This systematic literature review explores the application of ML and DL techniques in enhancing the diagnosis of brain tumors from MRI scans. ML and DL algorithms, particularly CNNs, have demonstrated significant success in automating the detection and classification of brain tumors by analyzing complex imaging patterns. The review follows PRISMA guidelines, synthesizing findings from studies between 2010 and 2025. Key themes include the utilization of diverse datasets, advanced feature extraction methods, and the computational efficiency of ML and DL models. Despite notable advancements, challenges such as data diversity and model interpretability persist, underscoring the need for ongoing research to optimize these techniques for enhanced clinical outcomes in brain tumor diagnosis. The review discusses the effectiveness of CNNs, SVMs, ensemble methods, transformers and other ML approaches in improving diagnostic accuracy and reliability. It also addresses future research directions aimed at overcoming current limitations and advances in the field.