ArticleIEEE sensors journal2021
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging.
Article in IEEE sensors journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 2 of them syntheses that pooled it.
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Who cites it
69 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Federated learning with swarm intelligence for efficient and secure medical image analysis.Scientific reports · 2026Article
- MedLedgerFL: a hybrid blockchain-federated learning framework for secure remote healthcare services.Scientific reports · 2026Article
- SENTINEL-Chain: a blockchain-integrated privacy-preserving framework for secure healthcare data publishing.Frontiers in digital health · 2026Article
- Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review.Current medical imaging · 2025Article
- False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions.Scientific reports · 2025Article
- Nature-Inspired Multi-Level Thresholding Integrated with CNN for Accurate COVID-19 and Lung Disease Classification in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Blockchain for Securing AI-Driven Healthcare Systems: A Systematic Review and Future Research Perspectives.Cureus · 2025Review
- A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.Scientific reports · 2025Article
- An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation.Scientific reports · 2025Article
- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- Federated learning as a smart tool for research on infectious diseases.BMC infectious diseases · 2024Review
- PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment.Sensors (Basel, Switzerland) · 2024Article
- Federated learning for medical image analysis: A survey.Pattern recognition · 2024Article
- IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm.Medical & biological engineering & computing · 2024Article
- A comparative study of federated learning methods for COVID-19 detection.Scientific reports · 2024Article
- An architecture for COVID-19 analysis and detection using big data, AI, and data architectures.PloS one · 2024Article
- A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine.Healthcare analytics (New York, N.Y.) · 2023Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.