Journal
IEEE ACCESS
Volume 7, Issue -, Pages 63605-63618Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2913847
Keywords
Alzheimer's disease; 3D-CNN; FSBi-LSTM; multi-modal fusion
Categories
Funding
- National Natural Science Foundation of China [61871274, 61801305, 81571758]
- National Natural Science Foundation of Guangdong Province [2017A030313377, 2016A030313047]
- Shenzhen Peacock Plan [KQTD2016053112051497, KQTD2015033016104926]
- Shenzhen Key Basic Research Project [JCYJ20170818142347251, JCYJ20170818094109846]
- Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) [MJUKF201711]
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Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a progressive state (i.e., pMCI) and a stable state (i.e., sMCI). With the development of deep learning, the convolutional neural networks (CNNs) have made great progress in image recognition using magnetic resonance imaging (MRI) and positron emission tomography (PET) for AD diagnosis. However, due to the limited availability of these imaging data, it is still challenging to effectively use CNNs for AD diagnosis. Toward this end, we design a novel deep learning framework. Specifically, the virtues of 3D-CNN and fully stacked bidirectional long short-term memory (FSBi-LSTM) are exploited in our framework. First, we design a 3D-CNN architecture to derive deep feature representation from both MRI and PET. Then, we apply FSBi-LSTM on the hidden spatial information from deep feature maps to further improve its performance. Finally, we validate our method on the AD neuroimaging initiative (ADNI) dataset. Our method achieves average accuracies of 94.82%, 86.36%, and 65.35% for differentiating AD from normal control (NC), pMCI from NC, and sMCI from NC, respectively, and outperforms the related algorithms in the literature.
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