期刊
IEEE ACCESS
卷 7, 期 -, 页码 80893-80901出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2919385
关键词
Alzheimer's Disease; Prediction; LSTM; Time Sequence; Magnetic Resonance Imaging
资金
- International Science and Technology Cooperation Project of Fujian Province of China [2019I0003]
- Science and Technology Planning Project of Quanzhou [2017G01]
- Online Course Supporting Project of Fujian [612-52418005, 612-50117024]
- Fundamental Research Funds for the Central Universities [20720190009]
Alzheimer's Disease (AD) is a chronic neurodegenerative disease. Early diagnosis will considerably decrease the risk of further deterioration. Unfortunately, current studies mainly focus on classifying the states of disease in its current stage, instead of predicting the possible development of the disease. Long short-term memory (LSTM) is a special kind of recurrent neural network, which might be able to connect previous information to the present task. Noticing that the temporal data for a patient are potentially meaningful for predicting the development of the disease, we propose a predicting model based on LSTM. Therefore an LSTM network, with fully connected layer and activation layers, is built to encode the temporal relation between features and the next stage of Alzheimer's Disease. The Experiments show that our model outperforms most of the existing models.
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