Predem: A computational framework for prediction of early dementia using deep neural networks

Abstract

This paper is focused on creating deep neural networks for the prediction and determination of dementia. It can be used in healthcare, research, and industrial applications. Impairment and dependency are closely related to dementia in older adults. We opted for dementia for our study, as there is no cure present to date for this disorder, and early diagnosis of dementia can help reduce symptoms and delay its progression. In this study, various neural networks were used to classify those affected with dementia from healthy controls by classifying Alzheimer's disease into four stages. For the predictions and classifications, we trained and designed various deep learning models, as the field of deep learning has proven to work well for classification tasks. Convolutional Neural Network, Recurrent Neural Network, and Visual Geometry Group were used to classify healthy controls from the dementia-affected subjects. The results showed that the VGG-16 method had the highest accuracy levels, followed by CNN and RNN. We used Alzheimer's dataset from ADNI on our CNN, RNN, and VGG-16 models. VGG-16 performed with the maximum accuracy of 89.5%, followed by CNN, which had an accuracy of 80.0%, and RNN with an accuracy of 70.2%