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首页> 外文期刊>IEEE sensors journal >Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
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Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

机译：使用CT成像的Covid-19检测区块链接 - 联邦学习和深度学习模型

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摘要

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.
机译：随着全球Covid-19案例的增加，需要一种有效的方法来诊断Covid-19患者。诊断Covid-19患者的主要问题是测试试剂盒的短缺和可靠性，由于病毒的快速传播，医学从业者面临难以识别阳性案例。第二个真实世界问题是在全球分享医院之间的数据，同时保持各组织的隐私问题。建立合作模式和保护隐私是培训全球深度学习模式的主要问题。本文提出了一个框架，该框架从不同来源（各医院）收集少量数据，并使用基于区块链的联合学习列举全球深度学习模型。 BlockChain Technology验证数据和联合学习在保留组织的隐私时全局策划该模型。首先，我们提出了一种数据归一化技术，该技术处理数据的异构性，因为数据从具有不同类型的计算机断层扫描仪（CT）扫描仪的不同医院收集。其次，我们使用基于胶囊网络的分割和分类来检测Covid-19患者。第三，我们设计一种可以使用BloctChain技术在保留隐私的同时使用BloctChain技术进行协作培训全球模型的方法。此外，我们收集了对研究界开放的现实生活Covid-19患者的数据。所提出的框架可以利用更新的数据，从而提高了CT图像的识别。最后，我们进行了全面的实验，以验证提出的方法。我们的结果表明了检测Covid-19患者的更好性能。

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  • 来源
    《IEEE sensors journal》 |2021年第14期|16301-16314|共14页
  • 作者

    Kumar Rajesh; Khan Abdullah Aman; Kumar Jay; Zakria; Golilarz Noorbakhsh Amiri; Zhang Simin; Ting Yang; Zheng Chengyu; Wang Wenyong;

  • 作者单位

    Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China;

    Univ Elect Sci & Technol China Chengdu 611731 Peoples R China;

    Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China;

    Univ Elect Sci & Technol China Chengdu 611731 Peoples R China;

    Univ Elect Sci & Technol China Chengdu 611731 Peoples R China;

    Sichuan Univ West China Hosp Dept Radiol Huaxi MR Res Ctr HMRRC Chengdu 610000 Peoples R China;

    Univ Elect Sci & Technol China Chengdu 611731 Peoples R China;

    China Telecom Co Ltd Sichuan Branch Chengdu 610000 Peoples R China;

    Macau Univ Sci & Technol Int Inst Next Generat Internet Taipa 999078 Macao Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    COVID-19; privacy-preserved data sharing; deep learning; federated-learning; blockchain;

    机译：Covid-19;隐私保留的数据共享;深度学习;联邦学习;区间;

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