Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review

• URL: http://arxiv.org/abs/2010.02154v1
• Date: Thu, 1 Oct 2020 06:38:06 GMT
• Title: Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review
• Authors: Shahabedin Nabavi (1), Azar Ejmalian (2), Mohsen Ebrahimi Moghaddam (1), Ahmad Ali Abin (1), Alejandro F. Frangi (3), Mohammad Mohammadi (4 and 5), Hamidreza Saligheh Rad (6) ((1) Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran. (2) Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. (3) Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK. (4) Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia, Australia. (5) School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia. (6) Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran.)
• Abstract summary: COVID-19 reveals signs in medical images can be used for early diagnosis of the disease even in asymptomatic patients. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.
• Score: 33.828866061570096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. Sometimes the symptoms of the disease increase so much they lead to the death of the patients. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. Many studies have tried to use medical imaging for early diagnosis of COVID-19. This study attempts to review papers on automatic methods for medical image analysis and diagnosis of COVID-19. For this purpose, PubMed, Google Scholar, arXiv and medRxiv were searched to find related studies by the end of April 2020, and the essential points of the collected studies were summarised. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis based on the accuracy and the method used, 4) to express the research limitations in this field and the methods used to overcome them. COVID-19 reveals signs in medical images can be used for early diagnosis of the disease even in asymptomatic patients. Using automated machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

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