A Comprehensive Survey of COVID-19 Detection Using Medical Images

• Published in: SN computer science Vol. 2; no. 6; p. 434
• Main Authors: Shah, Faisal Muhammad, Joy, Sajib Kumar Saha, Ahmed, Farzad, Hossain, Tonmoy, Humaira, Mayeesha, Ami, Amit Saha, Paul, Shimul, Jim, Md Abidur Rahman Khan, Ahmed, Sifat
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.11.2021; Springer Nature B.V
• Subjects: Artificial intelligence; Classification; Computed tomography; Computer Aided Methods to Combat COVID-19 Pandemic; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; COVID-19; Data Structures and Information Theory; Datasets; Feature extraction; Image enhancement; Information Systems and Communication Service; Medical imaging; Pattern Recognition and Graphics; Polymerase chain reaction; Preprocessing; Software Engineering/Programming and Operating Systems; Survey; Survey Article; Viral diseases; Vision; X-rays; COVID-19; Deep learning; Survey; Medical image; AI; X-ray; CT scan
• DOI: 10.1007/s42979-021-00823-1
• ISSN: 2662-995X; 2661-8907

The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.