AUTHOR=Durga R. , Poovammal E. 

TITLE=FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.892499

DOI=10.3389/fpubh.2022.892499

ISSN=2296-2565

ABSTRACT=With the SARS-COVID-19 virus's exponential growth, intelligent and constructive practise is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered as major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. Since medical data of the induvial is to be analyzed, another major problem of privacy and security to be addressed without any breach. To address these challenges, this paper proposes a novel framework based on blockchain and federated learning model. The federated learning model takes care of reduced complexity, blockchain helps in distributed data with privacy maintained. More precisely, the proposed Federated Learning Ensembled Deep Learning Block Chain Model (FLED- Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, performs the prediction accurately, keeps the right information while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) with the other learning models in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.