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

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.

A Blockchain‐Based Federated Learning Method for Smart Healthcare

“…To this end, Liu et al [ 15 ] proposed a blockchain-based secure FL framework for 5G networks, in which differential privacy noise was added on the updates to prevent inference attacks. Kumar et al [ 16 ] proposed a blockchain-based FL framework that trained a collaborative deep learning model for COVID-19 detection using clinical data from multiple hospitals and added Laplace noise to the local gradients to ensure privacy. Rahman et al [ 17 ] proposed a hybrid FL framework for the Internet of Health Things (IoHT) that supported lightweight DP to realize the privacy and anonymization of the IoHT data.…”

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

“…Another problem is privacy concerns in data sharing across different healthcare organizations. Solution based on CT images was proposed for detecting COVID-19 patients [168]. Firstly, they proposed a data normal-ization method that acts towards data heterogeneity as the data are collected from many healthcare organizations with various CT devices.…”

Role of Blockchain Technology in Combating COVID-19 Crisis

“…Frikha et al have proposed a blockchain-ethereum-based architecture which is mainly an integrated IoT blockchain web and mobile application to store and check electronic health records (EHRs). The proposal allows the patient and the medical staff to access health information securely [43].…”

“…Although various visions of adopting cutting-edge methods such as IoT and blockchain to improve combating Coronavirus have been investigated, the present study attempts to propose deep procedural specifications [44]; in [43], the researchers clarify their deep learning model technical employment in different parts. The explications that are mentioned above do not decrease the spread of the virus by the straightforward method of using blockchain.…”