Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model-Reference-Cited by-同舟云学术

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Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model

Published:2023 Issue: Volume: Page:52-63
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Akinlade Oluwatobi,Vakaj Edlira,Dridi Amna^ORCID,Tiwari Sanju^ORCID,Ortiz-Rodriguez Fernando^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-34222-6_5

Reference19 articles.

1. Ahmadi, M., Sharifi, A., Dorosti, S., Ghoushchi, S.J., Ghanbari, N.: Investigation of effective climatology parameters on covid-19 outbreak in Iran. Sci. Total Environ. 729, 138705 (2020)

2. Ai, T., et al.: Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (covid-19) in China: a report of 1014 cases. Radiology 296, E32–E40 (2020)

3. Bhandary, A.: Deep-learning framework to detect lung abnormality-a study with chest x-ray and lung CT scan images. Pattern Recogn. Lett. 129, 271–278 (2020)

4. Chen, Y., Sohel, F., Shah, S.A.A., Ding, S.: Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography. Optik 219, 164828 (2020)

5. Elnakib, A., Gimel’farb, G., Suri, J.S., El-Baz, A.: Medical image segmentation: a brief survey. In: El-Baz, A.S., Acharya U, R., Laine, A.F., Suri, J.S. (eds.) Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, pp. 1–39. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-8204-9_1

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1. Semantic Segmentation of Computed Tomography Scan of Lungs;2024 5th International Conference for Emerging Technology (INCET);2024-05-24

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