Classification of the level of alzheimer's disease using anatomical magnetic resonance images based on a novel deep learning structure

Abstract

Alzheimer’s is an incurable neurodegenerative disease that generally begins slowly and progresses gradually with time. In the early stage, symptoms of memory loss are mild, while in the late stage it clearly shows the deterioration in cognitive functions. Due to its irreversible nature, early detection reflects positively on reducing restraining the spread and preventing damage to the brain cells thus avoiding reaching the dementia stage. Till now, deep learning is considered to be one of the most significant methodologies used to detect and classify different types of neurological diseases from MRI images. However, in this study, we proposed a novel two-dimensional deep convolutional neural network to classify four stages of Alzheimer’s disease. The dataset consists of four types, namely nondemented, very mild demented, mild demented, and moderate demented subject MR images. First, we have applied a preprocessing technique to resize the image for compliance with our models. Then, we performed Reduce Atmospheric Haze techniques that can decrease the atmospheric haze making all images sharp and clear to feed to the model. We implemented the model 30 times and obtained more than 99.46% for evaluation metrics. The proposed method shows an outstanding performance compared to other papers reported in the literature.