Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches
This is an incremental review that synthesizes existing methods for diagnosing Alzheimer's disease using MRI and deep learning, potentially aiding researchers and clinicians in understanding current approaches.
The paper reviews five studies from 2021-2023 that use MRI-based deep learning algorithms to diagnose Alzheimer's disease, aiming to enable early detection and stage identification to improve patient care.
The most frequent kind of dementia of the nervous system, Alzheimer's disease, weakens several brain processes (such as memory) and eventually results in death. The clinical study uses magnetic resonance imaging to diagnose AD. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. As early diagnosis and stage detection are the most crucial elements in enhancing patient care and treatment outcomes, deep learning algorithms for MRI images have recently allowed for diagnosing a medical condition at the beginning stage and identifying particular symptoms of Alzheimer's disease. As a result, we aimed to analyze five specific studies focused on AD diagnosis using MRI-based deep learning algorithms between 2021 and 2023 in this study. To completely illustrate the differences between these techniques and comprehend how deep learning algorithms function, we attempted to explore selected approaches in depth.