文献类型：期刊文献

英文题名：Classification of Alzheimer's disease in MRI images using knowledge distillation framework: an investigation

作者：Li, Yiru[1];Luo, Jianxu[1];Zhang, Jiachen[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2022

卷号：17

期号：7

起止页码：1235

外文期刊名：INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000801161200002)】；

语种：英文

外文关键词：Alzheimer disease; MRI; Small samples; Deep learning; Classification; Knowledge distillation

摘要：Purpose Computer-aided MRI analysis is helpful for early detection of Alzheimer's disease(AD). Recently, 3D convolutional neural networks(CNN) are widely used to analyse MRI images. However, 3D CNN requires huge memory cost. In this paper, we introduce cascaded CNN and long and short-term memory (LSTM) networks. We also use knowledge distillation to improve the accuracy of the model using small medical image dataset. Methods We propose a cascade structure, CNN-LSTM. CNN is used as the function of feature extraction, and LSTM is used as the classifier. In this way, the correlation between different slices can be considered and the calculation cost caused by 3D data can be reduced. To overcome the problem of limited image training data, transfer learning is a more reasonable way of feature extraction. We use the knowledge distillation algorithm to improve the performance of student models for AD diagnosis through a powerful teacher model to guide the work of student models. Results The accuracy of the proposed model is improved using knowledge distillation. The results show that the accuracy of the student models reached 85.96% after the guidance of the teacher models, an increase by 3.83%. Conclusion We propose cascaded CNN-LSTM to classify 3D ADNI data, and use knowledge distillation to improve the model accuracy when trained with small size dataset. It can process 3D data efficiently as well as reduce the computational cost.