The Role of Imaging in the Detection and Management of COVID-19: A Review

Dong, Di; Tang, Zhenchao; Wang, Shuo; Hui, Hui; Gong, Lixin; Lu, Yao; Xue, Zhong; Liao, Hongen; Chen, Fang; Yang, Fan; Jin, Ronghua; Wang, Kun; Liu, Zhenyu; Wei, Jingwei; Mu, Wei; Zhang, Hui; Jiang, Jingying; Zhang, Dongliang

doi:10.1109/rbme.2020.2990959

Cited by 345 publications

(273 citation statements)

References 86 publications

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“…The virus was officially declared a global pandemic by the World Health Organisation (WHO) on 30th January 2020 [ 15 , 16 ]. Although new discoveries are being made at the time of writing this paper, the virus has been found to be highly contagious and this has led to its rapid spread throughout the world [ 17 ]. The virus is spread primarily through respiratory droplets from an infected person [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Review of the State of the Art in Non-Contact Sensing for COVID-19

Taylor

Abbasi

Dashtipour

et al. 2020

Sensors

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COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

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Section: Introductionmentioning

confidence: 99%

A Review of the State of the Art in Non-Contact Sensing for COVID-19

Taylor

Abbasi

Dashtipour

et al. 2020

Sensors

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“…A convolutional neural network is one of the most effective approaches in the diagnosis of lung diseases including COVID-19 directly from chest X-ray images. Several recent reviews have been carried out to highlight significant contributions to the detection of COVID-19 [19][20][21] . For instance, in 22 , a modified version of ResNet-50 pre-trained CNN model has been used to classify CT images into three classes: healthy, COVID-19 and bacterial pneumonia.…”

Section: Previous Work On Covid-19 Detection From Chest X-raymentioning

confidence: 99%

4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection

Abbas

Abdelsamea

Gaber

2020

Preprint

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Due to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for transfer learning (4S-DT) model.4S-DTencourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabelled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition layer to simplify the local structure of the data.4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream class-decomposition mechanism. We used 50,000 unlabelled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar.4S-DThas achieved an accuracy of97.54%in the detection of COVID-19 cases on an extended test set enriched by augmented images, out of which all real COVID-19 cases were detected, which was the highest accuracy obtained when compared to other methods.

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“…This approach has limitations just like the other one. It is difficult to understand whether pneumonia originates from corona or bacteria [13,14].…”

Section: Introductionmentioning

confidence: 99%

Detection and differentiation of COVID-19 using deep learning approach fed by x-rays

Erdaş

Ölçer

2020

International Journal of Applied Mathematics Electronics and Computers

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The coronavirus, which appeared in China in late 2019, spread over the world and became an epidemic. Although the mortality rate is not very high, it has hampered the lives of people around the world due to the high rate of spread. Moreover, compared to other individuals in the society, the mortality rate in elderly individuals and people with chronic disease is high. The early detection of infected individuals is one of the most effective ways to both fight disease and slow the outbreak. In this study, a deep learning approach, which is alternative and supportive of traditional diagnostic tools and fed with chest x-rays, has been developed. The purpose of this deep learning approach, which has the convolutional neural networks (CNNs) architecture, is (1) to diagnose pneumonia caused by a coronavirus, (2) to find out if a patient with symptoms of pneumonia on chest X-ray is caused by bacteria or coronavirus. For this purpose, a new database has been brought together from various publicly available sources. This dataset includes 50 chest X-rays from people diagnosed with pneumonia caused by a coronavirus, 50 chest X-rays from healthy individuals belonging to the control group, and 50 chest X-rays from people diagnosed with bacterium from pneumonia. Our approach succeeded in terms of accuracy of 92% for corona virus-based pneumonia diagnosis tasks (1) and 81% for the task of finding the origin of pneumonia (2). Besides, achievements for Area Under the ROC Curve (ROC_AUC), Precision, Recall, F1-score, Specificity, and Negative Predictive Value (NPV) metrics are specified in this paper.

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The Role of Imaging in the Detection and Management of COVID-19: A Review

Cited by 345 publications

References 86 publications

A Review of the State of the Art in Non-Contact Sensing for COVID-19

A Review of the State of the Art in Non-Contact Sensing for COVID-19

4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection

Detection and differentiation of COVID-19 using deep learning approach fed by x-rays

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