Data-Limited Deep Learning Methods for Mild Cognitive Impairment Classification in Alzheimer’s Disease Patients

Mild Cognitive Impairment (MCI) is the stage between the declining of normal brain function and the more serious decline of dementia. Alzheimer's disease (AD) is one of the leading forms of dementia. Although MCI does not always lead to AD, an early diagnosis of MCI may be helpful in finding those with early signs of AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has utilized magnetic resonance imaging (MRI) for the diagnosis of MCI and AD. MCI can be separated into two types: Early MCI (EMCI) and Late MCI (LMCI). Furthermore, MRI results can be separated into three views of axial, coronal and sagittal planes. In this work, we perform binary classifications between healthy people and the two types of MCI based on limited MRI images using deep learning approaches. Specifically, we implement and compare two various convolutional neural network (CNN) architectures. The MRIs of 516 patients were used in this study: 172 control normal (CN), 172 EMCI patients and 172 LMCI patients. For this data set, 50% of the images were used for training, 20% for validation, and the remaining 30% for testing. The results showed that the best classification for one model was between CN and LMCI for the coronal view with an accuracy of 79.67%. In addition, we achieved 67.85% accuracy for the second proposed model for the same classification group.