Structure-focused Neurodegeneration Convolutional Neural Network for Modeling and Classification of Alzheimer's Disease

Alzheimer's disease (AD), the predominant form of dementia, poses a growing global challenge and underscores the urgency of accurate and early diagnosis. The clinical technique radiologists adopt for distinguishing between mild cognitive impairment (MCI) and AD using Machine Resonance Imaging (MRI) encounter hurdles because they are not consistent and reliable. Machine learning has been shown to offer promise for early AD diagnosis. However, existing models focused on focal fine-grain features without considerations to focal structural features that give off information on neurodegeneration of the brain cerebral cortex. Therefore, this paper proposes a machine learning (ML) framework that integrates Gamma correction, an image enhancement technique, and includes a structure-focused neurodegeneration convolutional neural network (CNN) architecture called SNeurodCNN for discriminating between AD and MCI. The ML framework leverages the mid-sagittal and para-sagittal brain image viewpoints of the structure-focused Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Through experiments, our proposed machine learning framework shows exceptional performance. The parasagittal viewpoint set achieves 97.8% accuracy, with 97.0% specificity and 98.5% sensitivity. The midsagittal viewpoint is shown to present deeper insights into the structural brain changes given the increase in accuracy, specificity, and sensitivity, which are 98.1% 97.2%, and 99.0%, respectively. Using GradCAM technique, we show that our proposed model is capable of capturing the structural dynamics of MCI and AD which exist about the frontal lobe, occipital lobe, cerebellum, and parietal lobe. Therefore, our model itself as a potential brain structural change Digi-Biomarker for early diagnosis of AD.