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	Hybrid Feature Fusion Using RNN and Pre-trained CNN for Classification of Alzheimer's Disease
	Jabason, Emimal; Ahmad, M. Omair; Swamy, M. N. S.
通讯作者	Jabason, E (corresponding author), Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 1M8, Canada.
会议名称	22nd International Conference on Information Fusion (FUSION)
会议日期	JUL 02-05, 2019
会议地点	Ottawa, CANADA
英文摘要	The accurate classification of AD is very essential for both patient and social care, and it will be more significant once the treatment options are available to reverse the progress of the disease. The recent success of deep learning techniques has rapidly advanced the automatic classification of AD using neuroimaging biomarkers such as MRI. However, there exist two major challenges. First, training a deep convolutional neural network (CNN) from scratch relies on a large number of labeled training data to obtain high accuracy without overfitting. Second, due to high computational cost, most of the existing techniques employ 2D CNN that cannot leverage the complete spatial information; hence, it loses the inter-slice correlation. To address these limitations, we combine a recurrent neural network (RNN), specifically long short-term memory (LSTM) on top of the bottleneck layer of pre-trained DenseNet architecture, a deep CNN has already been trained on a large-scale dataset called ImageNet. In addition to the intra-slice features extracted from the deep CNN, the proposed technique exploits the inter-slice features through LSTM in order to discriminate the patients having AD and cognitively normal (CN) clinical status from the brain MRI data. Through experimental results, we show that our proposed model has better performance than state-of-the-art deep learning methods on the Open Access Series of Imaging Studies (OASIS) dataset using 5 -fold cross validation.
英文关键词	Alzheimer's disease (AD) Magnetic resonance imaging (MRI) Transfer learning DenseNet Long short-term memory (LSTM) Hybrid feature fusion
来源出版物	2019 22ND INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2019)
出版年	2019
ISBN	978-0-9964527-8-6
出版者	IEEE
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000567728800142
WOS关键词	NETWORKS ; IMAGE
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS研究方向	Computer Science ; Engineering
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369997
作者单位	[Jabason, Emimal; Ahmad, M. Omair; Swamy, M. N. S.] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 1M8, Canada
推荐引用方式 GB/T 7714	Jabason, Emimal,Ahmad, M. Omair,Swamy, M. N. S.. Hybrid Feature Fusion Using RNN and Pre-trained CNN for Classification of Alzheimer's Disease[C]:IEEE,2019.