Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (16): 253-262.

### 3D Transfer Learning Network for Classification of Alzheimer’s Disease

LU Xiaoling, WU Haifeng, ZENG Yu, KONG Lingxu, LUO Jinling

1. School of Electric & Informative Engineering, Yunnan Minzu University, Kunming 650504, China
• Online:2021-08-15 Published:2021-08-16

### 3D迁移网络的阿尔茨海默症分类研究

1. 云南民族大学 电气信息工程学院，昆明 650504

Abstract:

Alzheimer’s disease cannot be cured at present. If Alzheimer’s disease can be correctly diagnosed, a correct treatment will delay the patient’s condition. To reduce the time and cost of manual diagnosis for Alzheimer’s disease, this paper adopts machine learning to assistantly diagnose Alzheimer’s disease, and proposes a transfer learning method that uses 3D Magnetic Resonance Imaging（MRI） signals. The proposed method uses a separable convolutional network, MobileNet transfer network to complete the classification of Alzheimer’s disease and normal control. The method extracts preliminary features of slices from a raw MRI image on a bottleneck layer, reduces dimensions and extracts further features on a supervised training top layer, and finally combines and trains the features of all slices on a classification layer. The advantage of this transfer learning method to extract image features is that, it can reduce the training time of the network and improve the classification accuracy. In experiment, the proposed method is tested with a group of OASIS data. The experimental results show that compared with the traditional transfer learning network, the classification accuracy of the proposed method can be improved by about 8 percentage points, and the time can be reduced to 1/60.