A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification

• Published in: Magnetic resonance imaging Vol. 78; pp. 119 - 126
• Main Authors: Zhang, Jie, Zheng, Bowen, Gao, Ang, Feng, Xin, Liang, Dong, Long, Xiaojing
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.05.2021
• Subjects: Aged; Algorithms; Alzheimer Disease - classification; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Attention mechanism; Convolutional neural network; Databases, Factual; Deep Learning; Early detection; Early Diagnosis; Female; Humans; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging; Male; Structural MRI; Structural MRI; Attention mechanism; Alzheimer's disease; Convolutional neural network; Early detection
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2021.02.001

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or prediction on the conversion from mild cognitive impairment (MCI) to AD. In this study, we aimed to develop a new deep learning method to detect or predict AD in an efficient way. We proposed a densely connected convolution neural network with connection-wise attention mechanism to learn the multi-level features of brain MR images for AD classification. We used the densely connected neural network to extract multi-scale features from pre-processed images, and connection-wise attention mechanism was applied to combine connections among features from different layers to hierarchically transform the MR images into more compact high-level features. Furthermore, we extended the convolution operation to 3D to capture the spatial information of MRI. The features extracted from each 3D convolution layer were integrated with features from all preceding layers with different attention, and were finally used for classification. Our method was evaluated on the baseline MRI of 968 subjects from ADNI database to discriminate (1) AD versus healthy subjects, (2) MCI converters versus healthy subjects, and (3) MCI converters versus non-converters. The proposed method achieved 97.35% accuracy for distinguishing AD patients from healthy control, 87.82% for MCI converters against healthy control, and 78.79% for MCI converters against non-converters. Compared with some neural networks and methods reported in recent studies, the classification performance of our proposed algorithm was among the top ranks and improved in discriminating MCI subjects who were in high risks of conversion to AD. Deep learning techniques provide a powerful tool to explore minute but intricate characteristics in MR images which may facilitate early diagnosis and prediction of AD.