A systematic analysis and review of COVID-19 detection techniques using CT image

An unprecedented pandemic, named COVID-19, impacts the entire world and has been experienced in 2020. Due to the lack of treatment, all the researchers in each and every field concentrated to deal with it. Primarily in computer science, the contribution involves the development of approaches for detection, diagnosis and prediction of COVID-19 scenarios. In this field, Deep Learning (DL) and Data Science are the most extensively exploited approaches. This review outlines 50 research papers and also presents different approaches to identifying COVID-19. Here, these papers are classified and analysed into various categories and this survey presents details, like software tools employed, utilised datasets, published years and performance metrics, exploited in those papers. Moreover, the collected information is reviewed and graphical information regarding the result and analysis is presented. The research gaps and problems raised in conventional COVID-19 approaches are explained. For this review, the future work is on the basis of the research gaps and issues identified from the research strategies. Additionally, the result and analysis exhibit that the MATLAB software tool is extensively used for detection of COVID-19 and Convolutional Neural Network (CNN) model is the frequently utilised approach for COVID-19 detection.