A Novel End-to-End Hybrid Network for Alzheimer’s Disease Detection Using 3D CNN and 3D CLSTM

In this work, we will detect the Alzheimer’s disease using deep learning techniques (U Net architecture). Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. It is reported that there are 50 million people in the world suffering from dementia. 

Magnetic resonance imaging (MRI) plays an important role in Alzheimer’s disease (AD) detection as it shows morphological changes caused by brain atrophy. Convolutional neural network (CNN) has been successfully used to achieve good performance in accurate diagnosis of AD. 

The U-net is a convolutional network architecture for fast and precise segmentation of images mainly for medical images. Experiments will demonstrate that our model will provide best detection performance compared to the state-of-the-art methods.

Keywords: Alzheimer’s disease, Detection, Magnetic Resonance Images, Convolutional Neural Networks, U Net architecture.