Advances in Deep Learning for the Detection of Alzheimer’s Disease Using MRI—A Review

Magnetic Resonance Imaging (MRI) has played a vital role in comprehending brain functionalities and is a clinical tool in diagnosing neuro disorders like Alzheimer’s Disease (AD), Parkinson’s disease, and schizophrenia. Concurrently, massive amounts of data are generated beyond the capability of traditional data processing techniques. Analyzing these complex, high-dimensional data needs intelligent algorithms. Deep Learning technology has demonstrated high capability accuracy in image processing, natural language processing, object detection, and drug discovery. It learns features from data using backpropagation and changes its internal parameters to finally segment and classify an object. Similarly, it depends on the dataset for constructing the model. Several datasets exist to cater to the neuroimaging community for research advancements. fMRI is a subset of MRI technology that holds much promise in identifying neuro disorders, and deep learning technology has assisted in solving these complicated systems. This chapter discusses the latest works in the field of deep learning-assisted MRI identification of AD.