Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Abstract

Background and Objective The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors’ knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. Methods We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. Results The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. Conclusions The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.

On the role of artificial intelligence in medical imaging of COVID-19

“…Previous review papers on COVID-19 either focused on a technical assessment of AI in imaging 6 or elaborated on the role of imaging. 1 Related systematic reviews were either not devoted specifically to imaging 17,18 or used extremely small sample sizes (N = 11). 19 In contrast, this paper attempts to bridge clinical and technical perspectives by providing a comprehensive overview to guide researchers toward working on the most pressing problems in automating lung image analysis for COVID-19.…”

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

“…For example, [21] conducted an early literature survey on the detection of COVID-19 through machine learning approaches. Different deep learning approaches were discussed in the survey, including CNN variants such as the SqueezNet, mobilenet, Googlenet, VGG, Inception, Xception, Alexnet, Restnet, etc., and challenges were pointed out with suggestions for future works.…”

“…The experimental results showed that the existing supervised learning model was able to achieve high precision classifications and good qualitative visualization for the lesion detections. For a comprehensive review of existing machine learning models for COVID-19, interested readers are referred to the following references [11], [12], [21].…”

The research on COVID-19 and economy from 2019 to 2020: analysis from the perspective of bibliometrics

“…There are also some bibliometrics analyses that focus on some specific countries or regions (Gallegos et al, 2020;Raju & Patil, 2020) or publication sources (Oh & Kim, 2020). Beyond that, most of the bibliometrics analyses focus on some specific topics or fields, including COVID-19 and environment (Casado-Aranda et al, 2021;, mental health and COVID-19 (Maalouf et al, 2021), COVID-19 and machine learning (Chiroma et al, 2020;De Felice & Polimeni, 2020), COVID-19 and business and management (Rodrigues et al, 2020;Verma & Gustafsson, 2020), etc.…”