Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1779-1791.DOI: 10.3778/j.issn.1673-9418.2101091

• Network and Information Security • Previous Articles     Next Articles

System Model and Access Control Schemes for Medical Image Collaborative Analysis

LIU Tonglai1, ZHANG Zikai2, WU Jigang   

  1. 1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
    2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
  • Received:2021-01-20 Revised:2021-03-18 Online:2022-08-01 Published:2021-03-26
  • About author:LIU Tonglai, born in 1982, Ph.D. candidate, member of CCF. His research interests include blockchain, intelligent computing and data mining.
    ZHANG Zikai, born in 1989, Ph.D. candidate. His research interests include data security, network security and machine learning.
    WU Jigang, born in 1963, Ph.D., professor, Ph.D. supervisor, member of CCF. His research interests include intelligent computing and mobile computing.
  • Supported by:
    the National Natural Science Foundation of China(62072118);the Natural Science Foundation of Guangdong Province(2018B030311007)


刘同来1, 章子凯2, 武继刚   

  1. 1. 广东工业大学 计算机学院,广州 510006
    2. 北京交通大学 电子信息工程学院,北京 100044
  • 作者简介:刘同来(1982—),男,江苏连云港人,博士研究生,CCF会员,主要研究方向为区块链、智能计算、数据挖掘。
  • 基金资助:


Deep learning based medical image analysis has played an important role in the computer-aided diagnosis and treatment for diseases. The accuracy of classification has always been the primary goal pursued by researchers. However, the transmission process of images also faces the problems of limited bandwidth in WAN and increased risks of data security. Additionally, individual privacy is vulnerable when user data are exposed to an unauthorized user. To address these problems, this paper constructs a system model for collaborative analysis of diagnosis of diabetic retinopathy (DR). This model consists of two stages: data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the trained model into the blockchain, other private clouds can use the best-performing model shared by private clouds on the blockchain to identify the image quality and transfer high-quality image to the lesion classification model for use. In the classification stage of lesions, each private cloud uses high-quality images for classification and uploads its model parameters to the public cloud for aggregation to obtain a global model. Then, the public cloud sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transferred, and protect personal privacy. The access control scheme includes the improved role-based access control (RAC) used within the private cloud and the blockchain-based access control scheme (BAC) used during the interaction between the private cloud and the public cloud. RAC can grant both functional and data access permissions to roles, and consider object attributes to realize fine-grained control. BAC is based on certificateless public key cryptography technology and blockchain technology, which can realize identity authentication and permission identification of private cloud while requesting to transfer model parameters from private cloud to public cloud, protect the identity, permission and model parameters of private cloud, and achieve lightweight access control. Two retinal datasets are utilized for the classification of DR. Experimental results demonstrate that data cleaning can efficiently remove low quality images and improve the accuracy of the classifica-tion for early lesions of DR. The accuracy is up to 90.2%.

Key words: medical image, collaborative analysis, access control, data security, blockchain



关键词: 医疗图像, 协同分析, 访问控制, 数据安全, 区块链

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