Early diagnosis of Alzheimer's disease based on brain morphological changes: A comprehensive approach combining voxel-based morphometry and deep learning

Deep learning algorithms optimize data by enhancing resolution and suppressing noise associated with biological knowledge. The root issue is that, for example, CNNs learning mathematical patterns from statistical correlations in the data without regard to biological cues whatsoever, and merely apply filters such as max pooling, never grasping what the biological cues they are supposed to investigate are. This blind procedure can indeed be in technical language; however, it does not help to identify meaningful insights into neuroimaging, where interpretability is essential, and such inadequacies pose a grave challenge. In our research, rather than depending on the CNNs and FCNs only for the feature extractions, we have integrated biologically motivated features into voxel-based morphometry as well as deep learning. Our goal is to analyze T1-weighted MRI scans and T2-Flair images to investigate the characteristics of gray matter, white matter, cerebrospinal fluid, and white matter Hyperintensity in patients with mild cognitive impairment (MCI) who lie on the spectrum between normal aging and Alzheimer's disease (AD). So we extracted critical structural features such as white matter Hyperintensity, gray matter volume, white matter volume, cerebrospinal fluid (CSF) volume, and cortical thickness. These are biologically meaningful biomarkers that reflect the neurodegenerative alterations directly. To validate our method, after the detection of biological features, we have converted them into 3-bit, 4-bit, 8-bit, and 16-bit images. These images were used as inputs for both FCN and CNN models to investigate the early symptoms of AD from classified intracranial features.