Transfer Learning and Magnetic Resonance Imaging Techniques for the Deep Neural Network-Based Diagnosis of Early Cognitive Decline and Dementia

Abstract

Combining neuroimaging technologies and deep networks has gained considerable attention over the last few years. Instead of training deep networks from scratch, transfer learning methods have allowed retraining deep networks, which were already trained on massive data repositories, using a smaller dataset from a new application domain, and have demonstrated high performance in several application areas. In the context of a diagnosis of neurodegenerative disorders, this approach can potentially lessen the dependence of the training process on large neuroimaging datasets, and reduce the length of the training, validation, and testing process on a new dataset. To this end, the paper investigates transfer learning of deep networks, which were trained on ImageNet data, for the diagnosis of dementia. The designed networks are modifications of the AlexNet and VGG16 Convolutional Neural Networks (CNNs) and are retrained to classify Mild Cognitive Impairment (MCI), Alzheimer’s disease (AD) and normal patients using Diffusion Tensor Imaging (DTI) and Magnetic Resonance Imaging (MRI) data. An empirical evaluation using DTI and MRI data from the ADNI database supports the potential of transfer learning methods in the detection of early degenerative changes in the brain. Diagnosis of AD was achieved with an accuracy of 99.75% and a 0.995 Matthews correlation coefficient (MCC) score using transfer learning of VGG models retrained on DTI scans. Early cognitive decline was predicted with an accuracy of 93.88% and an MCC equal to 0.8602 by VGG models processing MRI data. The proposed models can be used as additional tools to support a quick and efficient diagnosis of MCI, AD and other neurodegenerative disorders.