Comparison of deep learning convolutional neural networks method with conventional volume-based morphometry measurement of hippocampal volume in Alzheimer's disease

Abstract

Dementia is a spectrum of diseases characterised by a progressive and irreversible decline in cognitive function. Appropriate tools and references are essential for evaluating individuals' cognitive levels, especially hippocampal volume, as it is the commonly used biomarker in detecting Alzheimer's disease (AD). It is important to note that while there is no cure for dementia, early intervention and support can greatly improve the lives of those affected. Our ongoing AD research is being conducted to develop new treatments and improve our understanding of the disease by using voxel-based morphometry (VBM) to compare sensitivity and specificity with the HippoDeep toolbox. We validated AD's hippocampal volume compared to age-matched healthy controls (HC) based on the HippoDeep Model by comparing it with VBM as the reference standard. Significant differences between hippocampal volume in AD and HC have been detected using VBM and HippoDeep analysis.