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Application with deep learning models for COVID-19 diagnosis

Year 2022, Volume: 5 Issue: 2, 169 - 180, 31.08.2022

Fuat Türk Yunus Kökver

https://doi.org/10.35377/saucis...1085625
Cited By: 3

Abstract

COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world. Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19. Many case classification studies face many problems, especially unbalanced and insufficient data. For this reason, deep learning methods have a great importance for the diagnosis of COVID-19. Therefore, we had the opportunity to study the architectures of NasNet-Mobile, DenseNet and Nasnet-Mobile+DenseNet with the dataset we have merged.
The dataset we have merged for COVID-19 is divided into 3 separate classes: Normal, COVID-19, and Pneumonia. We obtained the accuracy 87.16%, 93.38% and 93.72% for the NasNet-Mobile, DenseNet and NasNet-Mobile+DenseNet architectures for the classification, respectively. The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.

Keywords

COVID-19 diagnosis DenseNet NasNet-Mobile deep learning classification

References

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  • [10] G. Jia,H.Keung, L.Y.Xu, “Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method”, vol. 134, 2021. https://doi.org/10.1016/j.compbiomed.2021.104425
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  • [12] M. Gour, S. Jain. “Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification”, Computers in Biology and Medicine, vol. 140, 2022.
  • [13] H. Hassan, Z. Ren, H. Zhao, S. Huang, D. Li, S. Xiang, Y. Kang, S. Chen, B. Huang. “Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks”, vol. 141, 2020.
  • [14] T. Tuncer, F. Ozyurt, S. Dogan, A. Subasi. “A novel Covid-19 and pneumonia classification method based on F-transform”, Chemometrics and Intelligent Laboratory Systems, vol. 210, 2021.
  • [15] H.M. Balaha M. H. Balaha, H.A. Ali. “Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms”, Artificial Intelligence In Medicine, vol. 119, 2021. p 102156.
  • [16] R. Islam, Md. Nahiduzzaman “Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble-based machine learning approach”, Expert Systems with Applications, vol. 195, 2022.
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  • [18] H. Li, S. Zhuang, D. Li, J. Zhao, Y. Ma. “Benign and malignant classification of mammogram images based on deep learning”, Biomedical Signal Processing and Control, vol. 51, pp. 347-354, 2019.
  • [19] S. Vallabhajosyula, V. Sistla, V. Krishna, K. Kolli,” Transfer learning based deep ensemble neural network for plant leaf disease detection”,2021. https://doi.org/10.1007/s41348-021-00465-8.
  • [20] S.D. Deb, R.K. Jha, “Covid-19 detection from chest x-ray images using ensemble of cnn models, in: 2020 International Conference on Power,” Instrumentation, Control and Computing (PICC). IEEE, 2020; 1–5. doi: 10.1109/PICC51425.2020.9362499.
  • [21] J.P. Cohen, P. Morrison, L. Dao, K. Roth, T.Q. Duong, M. Ghassemi, “Covid-19 image data collection: Prospective predictions are the future 2020”, arXiv preprint arXiv: 2006.1198

Year 2022, Volume: 5 Issue: 2, 169 - 180, 31.08.2022

Fuat Türk Yunus Kökver

https://doi.org/10.35377/saucis...1085625
Cited By: 3

Abstract

References

  • [1] U.G. Kraemer Moritz et al., “Data curation during a pandemic and lessons learned from COVID-19.” Nat. Comput. Sci, vol. 1 (1), pp. 9–10, 2021.
  • [2] H. Panwar, P.K. Gupta, S. M. Khubeb, R.M. Menendez, P. Bhardwaj, V. Singh, “A Deep Learning and Grad-CAM based Color Visualization Approach for Fast Detection of COVID-19 Cases using Chest X-ray and CT-Scan Images,” Chaos, Solitons Fractals, vol. 140, 2020. https://doi.org/10.1016/j.chaos.2020.110190.
  • [3] P. Rai, B. K. Kumar, V. K. Deekshit, I. Karunasagar, “Detection technologies and recent developments in the diagnosis of COVID-19 infection.”, Appl. Microbiol. Biotechnol, pp. 1–15,2021. https://doi.org/10.1007/S00253- 020-11061
  • [4] C. C. Nathaniel et al., “Multiplexed detection and quantification of human antibody response to COVID-19 infection using a plasmon enhanced biosensor platform”, Biosens. Bioelectron, 171, pp. 112679-112679, 2021. https://doi.org/10.1016/J.BIOS.2020.112679.
  • [5] L. Fang, X. Wang. "Mathematical modelling of two-axis photovoltaic system with improved efficiency." Elektronika Ir Elektrotechnika, vol. 21. 4, pp 40-43, 2015.
  • [6] V. Manivel, A. Lesnewski, S. Shamim, G. carbonatto, T. Govindan, “CLUE: COVID-19 lung ultrasound in emergency department”, Emerg. Med. Australasia (EMA), vol. 32 (4), pp. 694–696, 2020.
  • [7] S. Yang, Y. Zhang, J. Shen, “Clinical potential of UTE-MRI for assessing COVID -19: patient- and lesion-based comparative analysis”, Magn. Reson. Imag., vol. 52 (2), pp. 397–406, 2020.
  • [8] A. Narin, C. Kaya, Z. Pamuk, “Automatic Detection of Coronavirus Disease (Covid19) Using X-Ray Images and Deep Convolutional Neural Networks,” arXiv preprint arXiv, pp.10849, 2020. https://doi.org/10.1007/s10044-021-00984
  • [9] L. Luo, Z. Luo, Y. Jia, C. Zhou, J. He, J. Lyu, X. Shen, “CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method”, BMC Pulm. Med., vol. 20 (11), pp. 719–739, 2020.
  • [10] G. Jia,H.Keung, L.Y.Xu, “Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method”, vol. 134, 2021. https://doi.org/10.1016/j.compbiomed.2021.104425
  • [11] P. Singh, M. Vallejo, I.M. El-Badawy, A. Aysha, J. Madhanagopal, A. Athif, M. Faudzi. “Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms”, Computers in Biology and Medicine, vol. 136, 2021.
  • [12] M. Gour, S. Jain. “Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification”, Computers in Biology and Medicine, vol. 140, 2022.
  • [13] H. Hassan, Z. Ren, H. Zhao, S. Huang, D. Li, S. Xiang, Y. Kang, S. Chen, B. Huang. “Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks”, vol. 141, 2020.
  • [14] T. Tuncer, F. Ozyurt, S. Dogan, A. Subasi. “A novel Covid-19 and pneumonia classification method based on F-transform”, Chemometrics and Intelligent Laboratory Systems, vol. 210, 2021.
  • [15] H.M. Balaha M. H. Balaha, H.A. Ali. “Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms”, Artificial Intelligence In Medicine, vol. 119, 2021. p 102156.
  • [16] R. Islam, Md. Nahiduzzaman “Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble-based machine learning approach”, Expert Systems with Applications, vol. 195, 2022.
  • [17] O. Russakovsky, J. Deng, H. Su, et al., “ImageNet large scale visual recognition challenge”, Int. J. Comput. Vis, vol. 115 (3), 2015, pp. 211–252.
  • [18] H. Li, S. Zhuang, D. Li, J. Zhao, Y. Ma. “Benign and malignant classification of mammogram images based on deep learning”, Biomedical Signal Processing and Control, vol. 51, pp. 347-354, 2019.
  • [19] S. Vallabhajosyula, V. Sistla, V. Krishna, K. Kolli,” Transfer learning based deep ensemble neural network for plant leaf disease detection”,2021. https://doi.org/10.1007/s41348-021-00465-8.
  • [20] S.D. Deb, R.K. Jha, “Covid-19 detection from chest x-ray images using ensemble of cnn models, in: 2020 International Conference on Power,” Instrumentation, Control and Computing (PICC). IEEE, 2020; 1–5. doi: 10.1109/PICC51425.2020.9362499.
  • [21] J.P. Cohen, P. Morrison, L. Dao, K. Roth, T.Q. Duong, M. Ghassemi, “Covid-19 image data collection: Prospective predictions are the future 2020”, arXiv preprint arXiv: 2006.1198
There are 21 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Articles
Authors

Fuat Türk 0000-0001-8159-360X

Yunus Kökver 0000-0002-9864-2866

Publication Date August 31, 2022
Submission Date March 10, 2022
Acceptance Date June 19, 2022
Published in Issue Year 2022Volume: 5 Issue: 2

Cite

IEEE F. Türk and Y. Kökver, “Application with deep learning models for COVID-19 diagnosis”, SAUCIS, vol. 5, no. 2, pp. 169–180, 2022, doi: 10.35377/saucis...1085625.

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