Deep Learning Techniques for Neurological Disorder Detection Using MRI: A Comprehensive Review

Abstract

Neurological diseases, such as neurodegenerative diseases, movement diseases, demyelinating diseases, and brain
tumors are a growing concern in the world health problem. Timely diagnosis and detection are the essential keys
to successful treatment, management of the patient, and enhancing the quality of life. Magnetic Resonance
Imaging (MRI) is not invasive, and it is a high-resolution modality offering an examination of structural and
functional change within the brain, thus being a fundamental pillar in clinical neurology. The conventional MRI
scan analysis is however time consuming, subjective and liable to inter-observer variation. Deep learning has
recently become an innovative technology in the field of medical imaging, which allows automatically
identifying, classifying, and segmenting various complex neurological disorders. This is a review that synthesizes
the latest developments in deep learning methods, such as convolutional neural networks, recurrent neural
networks, autoencoders, generative adversarial networks, and transfer learning, in MRI based neurological
disorder detection. Important uses in multiple sclerosis, brain tumor, Alzheimer, and Parkinson disease are
discussed. The article also highlights publicly accessible data, methodology issues, evaluation plans and research
topics in the future. This review brings together the findings of various studies and gives a hopeful view of the
transformative ability of deep learning in neuroimaging, at the same time as it points out the importance of
explainability, clinical validation, and ethical concerns.