Computer Vision for Covid-19 Control: A Survey

Ulhaq, Anwaar; Khan, Asim; Gomes, Douglas Pinto Sampaio; Paul, Manoranjan

doi:10.31224/osf.io/yt9sx

Cited by 73 publications

(63 citation statements)

References 69 publications

(62 reference statements)

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“…Previously, Ulhaq et al discussed the existing literature on deep learning-based computer vision algorithms relating to the diagnosis, prevention, and control of COVID-19. They also discussed the infection management and treatment-related deep learning algorithms along with a brief discussion about some of the existing datasets [12]. However, they included a pre-print (non peer-reviewed) version of some papers which limit the acceptability of the work.…”

Section: Related Workmentioning

confidence: 99%

“…Not just in the diagnosis of the virus, but there have been many works done with deep learning applied to almost all the sectors affected by the coronavirus. And the flood of such works have resulted in a number of surveys relating to the role of artificial intelligence in this pandemic situation covering not just diagnosis but also clinical management and treatment [12], image acquisition [13], infodemiology, infoveillance, drug and vaccine discovery [14], mathematical modelling [15], economic intervention [16] etc. including discussions of various datasets [12,13,[15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

Rahman¹,

Sarker²,

Miraj³

et al. 2020

Preprint

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The ravage of COVID-19 is not merely limited to taking its toll with half a million fatalities. It has halted the world economy, disrupting normalcy of lives with supervening severity than any other global catastrophe of the last few decades. The majority of the vaccine discovery attempts are still on trial, making early detection and containment the only feasible redress. The existing diagnostic technique with high accuracy has the setbacks of being expensive and sophisticated, requiring skilled individuals for specimen collection and screening resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captures the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 works done to date, methodical challenges along with probable solutions, and scopes of future exploration in this arena. We also provided a comparative quantitative analysis of the performance of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia from x-ray images. Our results show that Densenet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16\%, sensitivity: 98.93\%, specificity: 98.77\%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

Rahman¹,

Sarker²,

Miraj³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In [10] , the author presented a broad survey of computer vision methods to combat the challenge of the COVID-19 pandemic. Some examples of image segmentation and classification methods in COVID-19 applications are summarized in Table 1 along with results obtained of each approach.…”

Section: Introductionmentioning

confidence: 99%

Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

Oulefki

Agaian

Trongtirakul

et al. 2021

Pattern Recognition

159

108

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Highlights Automatically enhance, divide, and validate the COVID-19 CT images into regions with similar properties such as contrast and structure. Efficient Kapur entropy-based multilevel thresholding unsupervised procedure. Measure, visualize, and study comparisons of the infected by COVID-19 volume. The experiment results indicate that the proposed method can reach the desired heat-mapping of COVID-19 lesion and has the potential to be used for clinical application including developing country.

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“…Examples include diagnosis, clinical management and treatment, as well as the prevention and control of COVID-19 [13], [14].…”

Section: Introductionmentioning

confidence: 99%

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

Rezaei

Azarmi

2020

Preprint

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Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a Deep Neural Network-based Model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed DNN model along with an inverse perspective mapping technique leads to a very accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online infection risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

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Computer Vision for Covid-19 Control: A Survey

Cited by 73 publications

References 69 publications

Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

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