• Corpus ID: 215238421

Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

@article{Rajinikanth2020HarmonySearchAO,
  title={Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images},
  author={Venkatesan Rajinikanth and Nilanjan Dey and Alex Noel Joseph Raj and Aboul Ella Hassanien and K. C. Santosh and Nadaradjane Sri Madhava Raja},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.03431}
}
Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts; (ii) Image enhancement… 

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References

SHOWING 1-10 OF 32 REFERENCES

CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)

The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool; the CT visual quantitative analysis has high consistency and can reflect the clinical classification of CO VID-19.

Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia

Patients with fever and/or cough and with conspicuous ground-glass opacity lesions in the peripheral and posterior lungs on CT images, combined with normal or decreased white blood cells and a history of epidemic exposure, are highly suspected of having 2019 Novel Coronavirus (2019-nCoV) pneumonia.

CT manifestations of coronavirus disease-2019: A retrospective analysis of 73 cases by disease severity

Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study

Novel Coronavirus Infection (COVID-19) in Humans: A Scoping Review and Meta-Analysis

The majority of reported clinical symptoms and laboratory findings related to SARS-CoV-2 infection are non-specific and clinical suspicion, accompanied by a relevant epidemiological history, should be followed by early imaging and virological assay.

Coronavirus disease 2019: initial chest CT findings

Objectives To systematically analyze CT findings during the early and progressive stages of natural course of coronavirus disease 2019 and also to explore possible changes in pulmonary parenchymal

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection

With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, “crazy-paving” pattern and the “reverse halo” sign.

Chest Radiographic and CT Findings of the 2019 Novel Coronavirus Disease (COVID-19): Analysis of Nine Patients Treated in Korea

COVID-19 pneumonia in Korea primarily manifested as pure to mixed ground-glass opacities with a patchy to confluent or nodular shape in the bilateral peripheral posterior lungs.

Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19

The CT-SS could be used to evaluate the severity of pulmonary involvement quickly and objectively in patients with COVID-19 and was found to be optimal for identifying severe CO VID-19.

Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT

Radiologists in China and the United States distinguished COVID-19 from viral pneumonia on chest CT with high specificity but moderate sensitivity.