Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease

Abstract

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.

Keywords: Alzheimer’s disease; Classification; Convolutional neural network; Deep learning; Mild cognitive impairment.

Figure 1

Classification performance of SNeurodCNN model. The results combine performances at the midsagittal and parasagittal planes. Performance improvement with Gamma correction using the mid-sagittal plane.

Figure 2

Plot of the training and validation loss and accuracy curves generated during the learning process. Graphs (a,c) depict the graphs achieved by using parasagittal region-focused images while (b,d) depict the graphs achieved by the midsagittal region.

Figure 3

Plot of the receiver operating characteristic for parasagittal dataset (a) and Midsagittal dataset (b).

Figure 4

Regions of brain neurodegeneration. (a) Midsagittal brain internal regions (Source: ‘Internal Brain Regions’ by Casey Henley, licensed under CC BY-NC-SA 4.0 International License, (b) highlights of SNeurodCNN neurodegeneration sensitivity in the midsagittal (up) and parasagittal (down).

Figure 5

Visualization of regions of interest on the midsagittal plane identified by the CNN model in AD (a), pMCI (b) and sMCI patients (c) subjects.

Figure 6

Visualization of regions of interest on the parasagittal plane identified by the CNN model in AD patients (a), pMCI patients (b), and sMCI patients (c) subjects.

Figure 7

Visualising the performance of SNeurodCNN in comparison to the pre-trained models.

Figure 8

Overview of the proposed deep learning framework for AD diagnosis.

Figure 9

A chart of the average mini-mental state examination scores of participants across the disease states. The sMCI and pMCI have similar mini-mental scores which are significantly different from AD.

Figure 10

Architecture of the SNeurodCNN model.