Deep Learning and Federated Learning for Screening COVID-19: A Review

Abstract

Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated.

An Interactive Dashboard for Statistical Analysis of Intensive Care Unit COVID-19 Data

“…The evolution of this pandemic and its consequences were reflected in the mortality and morbidity of populations, leading to the development of several scientific studies and research projects all over the world [1][2][3][4][5][6][7][8][9][10][11]. For instance, the study carried out by Florensa et al [1] was carried out in Lleida, Spain, and compared the Alpha variant, first detected in the United Kingdom, and the Delta variant, first detected in India.…”