Application Of Artificial Intelligence In MRI Image Processing For Accurate Brain Tumor Diagnosis

Brain tumors remain one of the most life-threatening neurological disorders, requiring early and accurate diagnosis for effective treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the most widely used imaging technique for brain tumor detection due to its high resolution and ability to capture soft tissue details. However, manual interpretation of MRI scans is often time-consuming, subject to inter-observer variability, and prone to diagnostic errors. In recent years, Artificial Intelligence (AI), particularly machine learning and deep learning methods, has emerged as a promising tool for enhancing diagnostic accuracy in medical imaging. This study investigates the application of AI-based approaches, with a focus on convolutional neural networks (CNNs) and advanced architectures such as U-Net and ResNet, for automated detection and classification of brain tumors in MRI scans. A dataset of annotated MRI images was analyzed, and models were trained and validated using standardized performance metrics, including accuracy, sensitivity, specificity, and the area under the curve (AUC). Results demonstrated that AI models achieved diagnostic accuracies exceeding ۹۵%, significantly outperforming traditional image processing techniques. Moreover, AI-based segmentation methods improved tumor boundary detection, enabling more precise treatment planning. Despite these advancements, challenges such as data imbalance, computational cost, and generalizability across diverse populations remain. This research highlights the transformative role of AI in brain tumor diagnostics and suggests that its integration into clinical workflows can support radiologists in achieving faster, more reliable diagnoses. Future work should focus on multimodal imaging integration, explainable AI, and large-scale clinical trials to validate these technologies in real-world healthcare settings.

Keywords:

Artificial Intelligence , Brain Tumor , MRI , Deep Learning , Medical Imaging