Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 302-309.DOI: 10.11772/j.issn.1001-9081.2021020258

• Frontier and comprehensive applications • Previous Articles    

Weakly supervised fine-grained classification method of Alzheimer’s disease based on improved visual geometry group network

Shuang DENG(), Xiaohai HE, Linbo QING, Honggang CHEN, Qizhi TENG   

  1. College of Electronics and Information Engineering,Sichuan University,Chengdu Sichuan 610065,China
  • Received:2021-02-22 Revised:2021-04-28 Accepted:2021-04-29 Online:2021-05-12 Published:2022-01-10
  • Contact: Shuang DENG
  • About author:DENG Shuang, born in 1995, M. S. candidate. Her research interests include image processing, pattern recognition, artificial intelligence.
    HE Xiaohai, born in 1964, Ph. D., professor. His research interests include image processing, pattern recognition, image communication.
    QING Linbo, born in 1982, Ph. D., associate professor. His research interests include image processing, pattern recognition, video communication.
    CHEN Honggang, born in 1991, Ph. D., research assistant. His research interests include image/video understanding, restoration and compression coding.
    TENG Qizhi , born in 1961, Ph. D., professor. Her research interests include digital image processing, pattern recognition, 3D image reconstruction and analysis.
  • Supported by:
    Chengdu Major Science and Technology Application Demonstration Project(2019-YF09-00120-SN)


邓爽(), 何小海, 卿粼波, 陈洪刚, 滕奇志   

  1. 四川大学 电子信息学院,成都 610065
  • 通讯作者: 邓爽
  • 作者简介:邓爽 (1995—),女,四川绵阳人,硕士研究生,主要研究方向:图像处理、模式识别、人工智能
  • 基金资助:


In order to solve the problems of small difference of Magnetic Resonance Imaging (MRI) images between Alzheimer’s Disease (AD) patients and Normal Control (NC) people and great difficulty in classification of them, a weakly supervised fine-grained classification method for AD based on improved Visual Geometry Group (VGG) network was proposed. In this method, Weakly Supervised Data Augmentation Network (WSDAN) was took as the basic model, which was mainly composed of weakly supervised attention learning module, data augmentation module and bilinear attention pooling module. Firstly, the feature map and the attention map were generated through weakly supervised attention learning network, and the attention map was used to guide the data augmentation. Both the original image and the augmented data were used as the input data for training. Then, point production between the feature map and the attention map was performed by elements via bilinear attention pooling algorithm to obtain the feature matrix. Finally, the feature matrix was used as the input of the linear classification layer. Experimental results of applying WSDAN basic model with VGG19 as feature extraction network on MRI data of AD show that, compared with the WSDAN basic model, the proposed model only with image enhancement has the accuracy, sensitivity and specificity increased by 1.6 percentage points, 0.34 percentage points and 0.12 percentage points respectively; the model only using the improvement of VGG19 network has the accuracy and specificity improved by 0.7 percentage points and 2.82 percentage points respectively; the model combing the two methods above has the accuracy, sensitivity and specificity improved by 2.1 percentage points, 1.91 percentage points and 2.19 percentage points respectively.

Key words: improved Visual Geometry Group (VGG) network, weakly supervised, fine-grained classification, data augmentation, Alzheimer’s Disease (AD)



关键词: 改进VGG网络, 弱监督, 细粒度分类, 数据增强, 阿尔兹海默症

CLC Number: