Abstract
The increasing incidence of adult-onset dementia disorders and primarily Alzheimer’s disease (AD) among the aging population around the world is increasing the social and economic burden on society and healthcare systems. This paper presents three neural networking algorithms: MobileNet, Artificial Neural Networks (ANN), and DenseNet for AD classification based on MRI imaging data. The results of each model were compared in terms of performance metrics such as accuracy, true positive rate, and receiver operating curve values. Results mentioned that MNet classified AD progression with 95.41% of accuracy. Early detection and appropriate interventions, primarily on modifiable risk factors of AD, can delay the progression of cognitive impairment and other symptoms that represent a main trait of the disease.
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