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Alzheimer's disease classification method based on multi-modal medical images |
| HAN Kun1, PAN Haiwei1, ZHANG Wei2, BIAN Xiaofei1, CHEN Chunling1, HE Shuning1 |
1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
2. Modern Education Technology Center, Heilongjiang University, Harbin 150001, China |
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Abstract Multi-modal medical image information has been widely used in computer-aided detection and computer-aided diagnosis in the medical community. Feature information from multi-modal medical images can be used to accurately classify and diagnosis Alzheimer's disease (AD) characteristics. This paper presents a convolutional neural network model for 3D convolution operations on magnetic resonance imaging (MRI) and positron emission computed tomography (PET) images of Alzheimer's subjects to extract the feature information for the various modalities. Then, models are used to fuse these modal information sets into a rich multi-modal feature information dataset. Finally, this dataset is classified and predicted using a fully connected neural network. Tests on the public data set of the AD neuroimaging initiative show that this model more accurately evaluates accuracy (ACC) and area under the curve (AUC) conditions.
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| Keywords
Alzheimer's disease (AD)
magnetic resonance imaging (MRI)
positron emission computed tomography (PET)
image classification
deep learning
convolutional neural networks
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Issue Date: 17 June 2020
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2020,
Vol. 60
