Exploring the developments in brain MRI classification-Review study, challenges and future direction

Brain Magnetic Resonance Imaging (MRI) plays a crucial role in the diagnosis and treatment of various neurological disorders. With the growing availability of large-scale brain MRI datasets and advancements in machine learning techniques, automated classification of brain MRI scans has gained significant attention. This study provides a comprehensive overview of the state-of-the-art methods and recent advancements in brain MRI classification. The study aims to summarize the existing literature, highlight the challenges, and discuss the potential future directions in this field. The survey begins by introducing the principles of brain MRI and the data acquisition process, emphasizing the importance of standardized protocols. It then delves into the significance of automated brain MRI classification, including its potential impact on patient outcomes, treatment planning, and disease prognosis. The challenges associated with brain MRI classification, such as the complexity of brain structures and the variability in image acquisition, are discussed in detail. The limitations of these approaches, such as subjectivity and time-consuming processes, are highlighted. applications include the diagnosis and characterization of neurological disorders, such as brain tumors, Alzheimer's disease, and multiple sclerosis. The potential implications of automated classification in personalized medicine, treatment planning, and prognosis are also discussed. Lastly, the paper outlines future directions for brain MRI classification research. It explores emerging trends, such as the integration of advanced technologies like artificial intelligence and radiomics. The survey concludes by emphasizing the significance of continued research and collaboration to advance the field of brain MRI classification.