AUTHOR=Archana Kumari , Kaur Amandeep , Gulzar Yonis , Hamid Yasir , Mir Mohammad Shuaib , Soomro Arjumand Bano TITLE=Deep learning models/techniques for COVID-19 detection: a survey JOURNAL=Frontiers in Applied Mathematics and Statistics VOLUME=9 YEAR=2023 URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2023.1303714 DOI=10.3389/fams.2023.1303714 ISSN=2297-4687 ABSTRACT=

The early detection and preliminary diagnosis of COVID-19 play a crucial role in effectively managing the pandemic. Radiographic images have emerged as valuable tool in achieving this objective. Deep learning techniques, a subset of artificial intelligence, have been extensively employed for the processing and analysis of these radiographic images. Notably, their ability to identify and detect patterns within radiographic images can be extended beyond COVID-19 and can be applied to recognize patterns associated with other pandemics or diseases. This paper seeks to provide an overview of the deep learning techniques developed for detection of corona-virus (COVID-19) based on radiological data (X-Ray and CT images). It also sheds some information on the methods utilized for feature extraction and data preprocessing in this field. The purpose of this study is to make it easier for researchers to comprehend various deep learning techniques that are used to detect COVID-19 and to introduce or ensemble those approaches to prevent the spread of corona virus in future.