Review Article Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study S. Vineth Ligi , 1 Soumya Snigdha Kundu , 2 R. Kumar , 1 R. Narayanamoorthi , 3 Khin Wee Lai , 4 and Samiappan Dhanalakshmi 1 1 Department of Electronics and Communication Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu, Chennai, TN, India 2 Department of Computer Science Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu, Chennai, TN, India 3 Department of Electrical and Electronics Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu, Chennai, TN, India 4 Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia Correspondence should be addressed to Khin Wee Lai; lai.khinwee@um.edu.my and Samiappan Dhanalakshmi; dhanalas@ srmist.edu.in Received 13 August 2021; Revised 13 December 2021; Accepted 7 January 2022; Published 23 February 2022 Academic Editor: Cosimo Ieracitano Copyright © 2022 S. Vineth Ligi et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Pulmonary medical image analysis using image processing and deep learning approaches has made remarkable achievements in the diagnosis, prognosis, and severity check of lung diseases. e epidemic of COVID-19 brought out by the novel coronavirus has triggered a critical need for artificial intelligence assistance in diagnosing and controlling the disease to reduce its effects on people and global economies. is study aimed at identifying the various COVID-19 medical imaging analysis models proposed by different researchers and featured their merits and demerits. It gives a detailed discussion on the existing COVID-19 detection methodologies (diagnosis, prognosis, and severity/risk detection) and the challenges encountered for the same. It also highlights the various preprocessing and post-processing methods involved to enhance the detection mechanism. is work also tries to bring out the different unexplored research areas that are available for medical image analysis and how the vast research done for COVID-19 can advance the field. Despite deep learning methods presenting high levels of efficiency, some limitations have been briefly described in the study. Hence, this review can help understand the utilization and pros and cons of deep learning in analyzing medical images. 1. Introduction e pandemic brought forth by the coronavirus disease 2019 (COVID-19) not only sustains a devastating response on the well-being and health of the worldwide population but also demands a high rate of monitoring so that it does not extend on its destructive path. A vital aspect of the battle against COVID-19 is the efficient examination of the patients, which can help the infected receive quick treatment and immediate care. As of now, the customary screening process to identify COVID-19 is the reverse transcriptase-polymerase chain reaction (RT-PCR) test method. is test identifies the presence of SARS-CoV-2 ribonucleic acid (RNA) in respi- ratory specimen samples (obtained via a range of procedures such as the nasopharyngeal or oropharyngeal swabs) [1]. e RT-PCR test method, despite being effective, has a few shortcomings. It is time-consuming, complicated, and in- volves a lot of manual labor. All these concerns make it difficult to comb through the highly populated regions where millions have to be tested in a rapid norm. It is also Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 5998042, 25 pages https://doi.org/10.1155/2022/5998042