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

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

doi:10.1109/JSEN.2021.3076767

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

IEEE Sens J. 2021 Apr 30;21(14):16301-16314. doi: 10.1109/JSEN.2021.3076767. eCollection 2021 Jul 15.

Authors

Rajesh Kumar¹, Abdullah Aman Khan², Jay Kumar¹, Zakria², Noorbakhsh Amiri Golilarz², Simin Zhang³, Yang Ting², Chengyu Zheng⁴, Wenyong Wang⁵

Affiliations

¹ Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China Huzhou 313001 China.
² University of Electronic Science and Technology of China Chengdu 611731 China.
³ Huaxi MR Research Center (HMRRC)Department of RadiologyWest China Hospital of Sichuan University Chengdu 610000 China.
⁴ China TelecomCompany Ltd., Sichuan Branch Chengdu 610000 China.
⁵ International Institute of Next Generation Internet, Macau University of Science and Technology Taipa 999078 China.

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; blockchain; deep learning; federated-learning; privacy-preserved data sharing.

Grants and funding

This work was supported in part by the National Natural Science Foundation of China under Grant U2033212 and in part by the University of Electronic Science and Technology of China under Project Y03019023601016201.