The promise of convolutional neural networks for the early diagnosis of the Alzheimer's disease

Alzheimer's Disease (AD) is one of the most devastating neurologic disorders, if not the most, as there is no cure for this disease, and its symptoms eventually become severe enough to interfere with daily tasks. The early diagnosis of AD, which might be up to 8 years before the onset of dementia symptoms, comes with many promises. To this end, we propose a novel Convolutional Neural Network (CNN) as a cheap, fast, yet accurate solution. First, a gold-standard dataset, namely DARWIN, that was proposed for the detection of AD through handwriting and consisted of 1D features, was used to generate the 2D features, which were yielded into the proposed novel model. Then, the proposed novel model was trained and evaluated on this dataset. According to the experimental result, the proposed novel model obtained an accuracy as high as 90.4%, which was higher than the accuracies obtained by the state-of-the-art baselines, which covered a total of 17 widely-used classifiers.