Deep Learning of Texture and Structural Features for Multiclass Alzheimer's Disease Classification

This work proposes multiclass deep learning classification of Alzheimer's disease (AD) using novel texture and other associated features extracted from structural MRI. Two distinct learning models (Model 1 and 2) are presented where both include subcortical area specific feature extraction, feature selection and stacked auto-encoder (SAE) deep neural network (DNN). The models learn highly complex and subtle differences in spatial atrophy patterns using white matter volumes, gray matter volumes, cortical surface area, cortical thickness, and different types of Fractal Brownian Motion co-occurrence matrices for texture as features to classify AD from cognitive normal (CN) and mild cognitive impairment (MCI) in dementia patients. A five layer SAE with state-of-the-art dropout learning is trained on a publicly available ADNI dataset and the model performances are evaluated at two levels: one using in-house tenfold cross validation and another using the publicly available CADDementia competition. The in-house evaluations of our two models achieve 56.6% and 58.0% tenfold cross validation accuracies using 504 ADNI subjects. For the public domain evaluation, we are the first to report DNN to CADDementia and our methods yield competitive classification accuracies of 51.4% and 56.8%. Further, both of our proposed models offer higher True Positive Fraction (TPF) for AD class when compared to the top-overall ranked algorithm while Model 1 also ties for top diseased class sensitivity at 58.2% in the CADDementia challenge. Finally, Model 2 achieves strong disease class sensitivity with improvement in specificity and overall accuracy. Our algorithms have the potential to provide a rapid, objective, and non-invasive assessment of AD.