A machine learning approach to assist prediction of Alzheimer's disease with convolutional neural network.

Alzheimer's disease (AD) is an advanced form of dementia in which the brain's envelope contracts and gradually exhales. The disease impairs a person's ability to think and impairs social functioning. Human behaviour patterns change dramatically. An early symptom of the disorder is an inability to recall recent events or to hold a conversation. The current work seeks to analyse structural changes in brain images collected from different brain lobes of individuals suffering from AD. In addition, we are trying to decipher deep learning (DL) techniques to study the properties of brain images and use convolutional neural networks (CNNs) to predict early AD. Early prediction of such diseases is critical in saving lives and can lead to premature treatment and medical costs.