Early detection of Alzheimer's disease progression: comparative evaluation of deep learning models.

The accurate diagnosis and monitoring of Alzheimer's disease (AD) is particularly critical given the increasing number of cases worldwide. Improving forecasting precision using deep learning models on neuroimaging biomarkers can aid in more accurately predicting Alzheimer's associated disease progression. In this work, we assess two separate 3D Convolutional Neural Network (CNN) models for binary AD progression classification based on MRIs of the brain's structure. The first model uses a whole volume approach and processes entire MRI scans, thus requiring little computational power and minimal preprocessing compared to other methods. Alternatively, the second model applies voxel-level scrutiny by examining specific pre-defined brain regions that have statistically significant grey matter volume differences from cohort analyses. MRI preprocessing includes N4 bias field correction, segmentation of tissues, alignment to the Montreal Neurological Institute (MNI) space, and Gaussian smoothing for homogenization of image quality. For the region-focused model, feature extraction is driven by neuroanatomy, concentrating on areas where AD shows shrinkage changes. The full-volume CNN achieved a 94% validation accuracy, demonstrating high computational efficiency with its simpler architecture, while the region-guided model reached 95% accuracy by leveraging more complex domain-specific structural biomarkers, highlighting enhanced performance at the cost of increased model intricacy. This study highlights the potential of combining deep learning frameworks with neuroimaging biomarkers to improve early detection and monitoring of AD. While our findings highlight the value of guided feature selection and volumetric data evaluation in improving diagnostic precision, they are derived solely from the ADNI dataset and must be validated on more diverse clinical populations.