A Single Model Deep Learning Approach for Alzheimer's Disease Diagnosis

Abstract

Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients' life. The emerging computer-aided diagnostic methods that combine deep learning with structural magnetic resonance imaging (sMRI) have achieved encouraging results, but some of them are limit of issues such as data leakage, overfitting, and unexplainable diagnosis. In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD. This approach has the following differences from the current approaches: (1) Convolutional Neural Network (CNN) models of different structures and capacities are evaluated systemically and the most suitable model is adopted for AD diagnosis; (2) A data augmentation strategy named Two-stage Random RandAugment (TRRA) is proposed to alleviate the overfitting issue caused by limited training data and to improve the classification performance in AD diagnosis; (3) An explainable method of Grad-CAM++ is introduced to generate the visually explainable heatmaps to make our model more transparent. Our approach has been evaluated on two publicly accessible datasets for two classification tasks of AD vs. cognitively normal (CN) and progressive MCI (pMCI) vs. stable MCI (sMCI). The experimental results indicate that our approach outperforms the state-of-the-art approaches, including those using multi-model and three-dimensional (3D) CNN methods. The resultant heatmaps from our approach also highlight the lateral ventricle and some regions of cortex, which have been proved to be affected by AD.

Keywords: Alzheimer's disease diagnosis; data augmentation; explainable deep learning model; mild cognitive impairment.