Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

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

【Source】IEEE SENSORS JOURNAL

【Abstract】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.

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

【标题】使用 CT 成像检测 COVID-19 的区块链联邦学习和深度学习模型

【摘要】随着全球 COVID-19 病例的增加，需要一种有效的方法来诊断 COVID-19 患者。诊断 COVID-19 患者的主要问题是检测试剂盒的短缺和可靠性，由于病毒的快速传播，医生在识别阳性病例方面面临困难。第二个现实问题是在全球医院之间共享数据，同时考虑组织的隐私问题。构建协作模型和保护隐私是训练全球深度学习模型的主要关注点。本文提出了一个框架，该框架从不同来源（各个医院）收集少量数据，并使用基于区块链的联邦学习训练全球深度学习模型。区块链技术对数据进行身份验证，联邦学习在全球范围内训练模型，同时保护组织的隐私。首先，我们提出了一种数据标准化技术，该技术处理数据的异质性，因为数据是从拥有不同类型计算机断层扫描 (CT) 扫描仪的不同医院收集的。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，我们设计了一种方法，该方法可以使用区块链技术和联邦学习协同训练全局模型，同时保护隐私。此外，我们收集了对研究界开放的真实 COVID-19 患者数据。所提出的框架可以利用最新数据，从而提高 CT 图像的识别能力。最后，我们进行了全面的实验来验证所提出的方法。我们的结果表明检测 COVID-19 患者的性能更好。

【关键词】新冠肺炎;保护隐私的数据共享；深度学习；联邦学习；区块链

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】4.325

【翻译者】石东瑛