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 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 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 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, which is, open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of computed tomography (CT) images. Finally, our results demonstrate a better performance to detect COVID-19 patients.

arXiv admin note: text overlap with arXiv:2003.10849 by other authors

References in corpus (7)

• Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
• CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
• COVID-CT-Dataset: A CT Scan Dataset about COVID-19
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning
• BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond
• Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT

Cited by in corpus (14)

• Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey
• Federated Learning: Opportunities and Challenges
• A Systematic Survey of Blockchained Federated Learning
• Dynamic Fusion based Federated Learning for COVID-19 Detection
• Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
• Model-Contrastive Federated Learning
• Federated Learning for Smart Healthcare: A Survey
• Implicit Model Specialization through DAG-based Decentralized Federated Learning
• Blockchain-based Trustworthy Federated Learning Architecture
• COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework
• Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain
• Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans
• DPCOVID: Privacy-Preserving Federated Covid-19 Detection
• A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging