(untitled) (Eureka Journal of Health Sciences & Medical Innovation, 2026)
Brain tumors are a leading cause of morbidity and mortality worldwide, and early, accurate diagnosis is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is the standard imaging modality for brain tumor detection, providing high-resolution visualization of brain structures and pathological changes. However, manual interpretation of MRI scans is time-consuming and dependent on radiologist expertise, leading to variability in diagnosis. Artificial intelligence (AI) and deep learning approaches, particularly convolutional neural networks (CNNs), offer automated, precise, and efficient solutions for brain tumor detection and classification. This paper reviews current AI methodologies for brain tumor analysis using MRI, highlighting detection, segmentation, and classification models. Challenges such as limited annotated datasets, variability in imaging protocols, and interpretability of models are discussed. The study emphasizes the potential of AI systems to enhance diagnostic accuracy, optimize workflow, and improve patient care in neuro-oncology.