AUTHOR=Pan Dan , Zeng An , Jia Longfei , Huang Yin , Frizzell Tory , Song Xiaowei 

TITLE=Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00259

DOI=10.3389/fnins.2020.00259

ISSN=1662-453X

ABSTRACT=Early detection is critical for effective management of Alzheimer’s disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb processing efficiency with use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL to identify subjects with MCI or AD using MRI: i.e., classification between 1) AD and healthy cognition (HC), 2) MCIc (MCI patients who will convert to AD) and HC, and 3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified 5-fold cross-validations. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice-sets, transformed into the standard MNI (Montreal Neurological Institute) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.81±0.03, 0.79±0.04, and 0.62±0.06 respectively for classifying AD vs. HC, MCIc vs. HC, and MCIc vs MCInc, comparable to previous reports. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that the combined CNN and EL approach can successfully capture AD related brain variations early in the disease process.