DeepAD: Alzheimer′s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI

Abstract

1To extract patterns from neuroimaging data, various techniques, including statistical methods and machine learning algorithms, have been explored to ultimately aid in Alzheimers disease diagnosis of older adults in both clinical and research applications. However, identifying the distinctions between Alzheimers brain data and healthy brain data in older adults (age > 75) is challenging due to highly similar brain patterns and image intensities. Recently, cutting-edge deep learning technologies have been rapidly expanding into numerous fields, including medical image analysis. This work outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimers magnetic resonance imaging (MRI) and functional MRI data from normal healthy control data for the same age group. Using these pipelines, which were executed on a GPU-based high performance computing platform, the data were strictly and carefully preprocessed. Next, scale and shift invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, functional MRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimers disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output when compared to other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively. Additionally, the subject-level classification was performed that resulted in the averaged accuracy rate of 94.32% and 97.88% for the fMRI and MRI pipeline respectively. Finally, a decision making algorithm was designed for the subject-level classification and improved the averaged accuracy rate to 97.77% for fMRI and 100% MRI subjects.