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Application with deep learning models for COVID-19 diagnosis

Yıl 2022, , 169 - 180, 31.08.2022

Fuat Türk Yunus Kökver

https://doi.org/10.35377/saucis...1085625
Cited By: 4

Öz

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.

Anahtar Kelimeler

COVID-19 diagnosis DenseNet NasNet-Mobile deep learning classification

Kaynakça

  • [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

Yıl 2022, , 169 - 180, 31.08.2022

Fuat Türk Yunus Kökver

https://doi.org/10.35377/saucis...1085625
Cited By: 4

Öz

Kaynakça

  • [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
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Makaleler
Yazarlar

Fuat Türk 0000-0001-8159-360X

Yunus Kökver 0000-0002-9864-2866

Yayımlanma Tarihi 31 Ağustos 2022
Gönderilme Tarihi 10 Mart 2022
Kabul Tarihi 19 Haziran 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

IEEE F. Türk ve Y. Kökver, “Application with deep learning models for COVID-19 diagnosis”, SAUCIS, c. 5, sy. 2, ss. 169–180, 2022, doi: 10.35377/saucis...1085625.

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