  Alzheimer's disease (AD), the predominant form of dementia, is a growing
global challenge, emphasizing the urgent need for accurate and early diagnosis.
Current clinical diagnoses rely on radiologist expert interpretation, which is
prone to human error. Deep learning has thus far shown promise for early AD
diagnosis. However, existing methods often overlook focal structural atrophy
critical for enhanced understanding of the cerebral cortex neurodegeneration.
This paper proposes a deep learning framework that includes a novel
structure-focused neurodegeneration CNN architecture named SNeurodCNN and an
image brightness enhancement preprocessor using gamma correction. The
SNeurodCNN architecture takes as input the focal structural atrophy features
resulting from segmentation of brain structures captured through magnetic
resonance imaging (MRI). As a result, the architecture considers only necessary
CNN components, which comprises of two downsampling convolutional blocks and
two fully connected layers, for achieving the desired classification task, and
utilises regularisation techniques to regularise learnable parameters.
Leveraging mid-sagittal and para-sagittal brain image viewpoints from the
Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our framework
demonstrated exceptional performance. The para-sagittal viewpoint achieved
97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the
mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2%
specificity, and 99.0% sensitivity. Model analysis revealed the ability of
SNeurodCNN to capture the structural dynamics of mild cognitive impairment
(MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and
parietal lobe, suggesting its potential as a brain structural change
digi-biomarker for early AD diagnosis. This work can be reproduced using code
we made available on GitHub.
