Implementation of SMOTE and VGG-16 for COVID-19 Radiography-Reference-Cited by-同舟云学术

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Implementation of SMOTE and VGG-16 for COVID-19 Radiography

Published:2022 Issue: Volume: Page:511-518
ISSN:2367-3370
Container-title:Intelligent Systems and Networks
language:
Short-container-title:

Author:

Dang Xuan Tho,Dao Nam Anh

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-3394-3_58

Reference21 articles.

1. Shi, H., Han, X., et al.: (2020) Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 20(4), 425–434 (2020)

2. Bacellar, G., Chandrappa, M., et al.: COVID-19 Chest X-Ray Image Classification Using Deep Learning, medRxiv 2021.07.15.21260605 (2021)

3. Alizad-Rahvar, A.R., Vafadar, S., et al.: False negative mitigation in group testing for COVID-19 screening. Front. Med. 8, 579 (2021)

4. Ortiz, A., Trivedi, A., et al.: Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes. Sci. Rep. 12, 1716 (2022)

5. Tho, D.X., Anh, D.N.: Deep learning-based imbalanced data classification for chest X-Ray image analysis. In: The International Conference on Intelligent Systems & Networks, pp. 109–115 (2021)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. G-DCNN: GAN based Deep 2D-CNN for COVID-19 Classification;2022 International Visualization, Informatics and Technology Conference (IVIT);2022-11-01

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