Convolutional Autoencoder based Deep Learning Approach for Alzheimer's Disease Diagnosis using Brain MRI

Rapid and accurate diagnosis of Alzheimer's disease (AD) is critical for patient treatment, especially in the early stages of the disease. While computer-assisted diagnosis based on neuroimaging holds vast potential for helping clinicians detect disease sooner, there are still some technical hurdles to overcome. This study presents an end-to-end disease detection approach using convolutional autoencoders by integrating supervised prediction and unsupervised representation. The 2D neural network is based upon a pre-trained 2D convolutional autoencoder to capture latent representations in structural brain magnetic resonance imaging (MRI) scans. Experiments on the OASIS brain MRI dataset revealed that the model outperforms a number of traditional classifiers in terms of accuracy using a single slice.