An Overview of Deep Learning in Medical Imaging Focusing On MRI - Mr J. P. Pramod; Kali Sravanthi; Chilpoori Srivani (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
Deep learning has impacted medical imaging quite a lot, and there is potential to advance the detection and treatment of intricate disorders. MRI is unique among imaging modalities because it is capable of detecting subtle contrasts in soft tissue without the application of ionizing radiation. To aim for better enhanced medical diagnosis, the present paper introduces a fresh analysis of MRI data and the integration of deep learning models. This study shows that advanced neural networks, e.g., Generative Adversarial Networks (GANs) for image reconstruction and U-Net for segmentation, can be employed to make diagnosis automatic, minimize the risk of human error, and facilitate early disease diagnosis of diseases like brain tumors, multiple sclerosis, and neurodegenerative diseases. The study also points to challenges regarding interpretability, lack of data, and the ethical aspects of AI-based