Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images

Durmuş Özdemir; Naciye Nur Arslan

doi:10.29130/dubited.976118

TR EN

Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images

Abstract

This study aimed to present an analysis of deep transfer learning models to support the early diagnosis of Covid-19 disease using X-ray images. For this purpose, the deep transfer learning models VGG-16, VGG-19, Inception V3 and Xception, which were successful in the ImageNet competition, were used to detect Covid-19 disease. Also, 280 chest x-ray images were used for the training data, and 140 chest x-ray images were used for the test data. As a result of the statistical analysis, the most successful model was Inception V3 (%92), the next successful model was Xception (%91), and the VGG-16 and VGG-19 models gave the same result (%88). The proposed deep learning model offers significant advantages in diagnosing covid-19 disease issues such as test costs, test accuracy rate, staff workload, and waiting time for test results.

Keywords

Biomedical Informatics, Deep Learning, Covid-19 Diagnosis, Image Classification

References

[1] Z. Y. Zu, M. D. Jiang, P. P. Xu, W. Chen, Q. Q. Ni, G. M. Lu, and L. J. Zhang, “Coronavirus Disease 2019 (COVID-19): A perspective from China,” Radiology, vol. 296, pp. 15-25, 2020.
[2] T. Singhal, “Review on COVID19 disease so far,” Indian J. Pediatr., vol. 87, no. 5, pp. 281-286, 2020.
[3] A. Hamimi, “MERS-CoV: Middle East respiratory syndrome corona virus: can radiology be of help? Initial single center experience,” Egypt. J. Radiol. Nucl. Med., vol. 47, no. 1, pp. 95-106, 2016.
[4] D. Wu, K. Gong, C. D. Arru, F. Homayounieh, B. Bizzo, V. Buch, H. Ren, K. Kim, N. Neumark, P. Xu, Z. Liu, W. Fang, N. Xie, W. Y. Tak, S. Y. Park, Y. R. Lee, M. K. Kang, J. G. Park, A. Carriero, L. Saba, M. Masjedi, H. Talari, R. Babaei, H. K. Mobin, S. Ebrahimian, I. Dayan, M. K. Kalra, and Q. Li , “Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 12, pp. 3529–3538, 2020.
[5] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Comput. Biol. Med., vol. 121, p. 103792, 2020.
[6] H. X. Bai, B. Hsieh, Z. Xiong, K. Halsey, J. W. Choi, T. M. L. Tran, I. Pan, L. -B. Shi, D. -C. Wang, J. Mei, X. Jiang, Q. -H. Zeng, T. K. Egglin, P. Hu, S. Agarwal, F. -F. Xie, S. Li, T. Healey, M. K. Atalay, and W. -H. Liao, “Performance of radiologists in differentiating COVID-19 from non COVID-19 viral pneumonia at chest CT,” Radiology, vol. 296, no. 2, pp. 46-54, 2020.
[7] T. Nihashi, T. Ishigaki, H. Satake, S. Ito, O. Kaii, Y. Mori, K. Shimamoto, H. Fukushima, K. Suzuki, H. Umakoshi, M. Ohashi, F. Kawaguchi, and S. Naganawa, “Monitoring of fatigue in radiologists during prolonged image interpretation using fNIRS,” Jpn. J. Radiol., vol. 37, no. 6, pp. 437-448, 2019.
[8] L. Salvador-Carulla, S. Rosenberg, J. Mendoza, H. Tabatabaei-Jafari, and P.-M. H. I. Network, “Rapid response to crisis: Health system lessons from the active period of COVID-19,” Heal. Policy Technol., vol.9, no. 4, pp. 578-586, 2020.
[9] A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks,” Comput. Biol. Med., vol. 121, p. 103795, 2020.
[10] W. K. Silverstein, L. Stroud, G. E. Cleghorn, and J. A. Leis, “First imported case of 2019 novel coronavirus in Canada, presenting as mild pneumonia,” Lancet, vol. 395, p. 734, 2020.

[11] S. Belciug, S.-I. Bejinariu, and H. Costin, “An Artificial Immune System Approach for a Multi-compartment Queuing Model for Improving Medical Resources and Inpatient Bed Occupancy in Pandemics,” Adv. Electr. Comput. Eng., vol. 20, no. 3, pp. 23-30, 2020.
[12] G. E. Güraksın, S. Barın, E. Özgül, and K. Furkan, “COVID-19 diagnosis using deep learning,” Düzce Üniversitesi Bilim ve Teknol. Derg., vol. 9, no. 3, pp. 8-23, 2021.
[13] M. A. AlMulla, “Location-based Expert System for Diabetes Diagnosis,” Kuwait J. Sci., vol. 48, no. 1, pp. 67-77, 2021.
[14] S. B. Desai, A. Pareek, and M. P. Lungren, “Deep learning and its role in COVID-19 medical imaging,” Intell. Med., vol. 3, p. 100013, 2020.
[15] K. bıçakcı and V. tunalı, “COVID-19 prediction from Chest X-Ray images using transfer learning,” Düzce Üniversitesi Bilim ve Teknol. Derg., vol. 9, no. 4, pp. 1395-1407, 2021.
[16] Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, S. Singh, and P. K. Shukla, “Deep Transfer Learning Based Classification Model for COVID-19 Disease,” IRBM, vol. 43, no. 2, pp. 87-92, 2022.
[17] L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone, “Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays,” Comput. Methods Programs Biomed., vol. 196, p. 105608, 2020.
[18] H. Panwar, P. K. Gupta, M. K. Siddiqui, R. Morales-Menendez, and V. Singh, “Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet,” Chaos, Solitons and Fractals, vol. 138, p. 109944, 2020.
[19] T. B. Alakus and I. Turkoglu, “Comparison of deep learning approaches to predict COVID-19 infection,” Chaos, Solitons and Fractals, vol. 140, p. 110120, 2020.
[20] S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. Jamalipour Soufi, “Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning,” Med. Image Anal., vol. 65, p. 101794, 2020.
[21] A. I. Khan, J. L. Shah, and M. M. Bhat, “CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images,” Comput. Methods Programs Biomed., vol. 196, p. 105581, 2020.
[22] J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “Covid-19 image data collection: Prospective predictions are the future,” 2020, arXiv Prepr. arXiv2006.11988, 2020.
[23] N. K. Chowdhury, M. M. Rahman, and M. A. Kabir, “PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images,” Heal. Inf. Sci. Syst., vol. 8, no. 1, pp. 1-14, 2020.
[24] R. Al-Hmouz, “Deep learning autoencoder approach: Automatic recognition of artistic Arabic calligraphy types,” Kuwait J. Sci., vol. 47, no. 3, 2020.
[25] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436- 444, 2015.
[26] A. Sufian, A. Ghosh, A. S. Sadiq, and F. Smarandache, “A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic,” J. Syst. Archit., vol. 108, p. 101830, 2020.
[27] X. Bai, X. Wang, X. Liu, Q. Liu, J. Song, N. Sebe, and B Kim, “Explainable Deep Learning for Efficient and Robust Pattern Recognition: A Survey of Recent Developments,” Pattern Recognit., vol. 120 p. 108102, 2021.
[28] Y. Liang, W. Peng, Z.-J. Zheng, O. Silvén, and G. Zhao, “A hybrid quantum–classical neural network with deep residual learning,” Neural Networks, vol. 143, pp. 133-147, 2021.
[29] S. Akcay and T. Breckon, “Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging,” Pattern Recognit., vol. 122, p. 108245, 2021.
[30] H. Shin, H. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285-1298, 2016.
[31] A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An ensemble of fine-tuned convolutional neural networks for medical image classification,” IEEE J. Biomed. Heal. informatics, vol. 21, no. 1, pp. 31-40, 2016.
[32] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet large scale visual recognition challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, 2015.
[33] J. Deng, W. Dong, R. Socher, L. -J. Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255.
[34] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015.
[35] R. A. Aral, Ş. R. Keskin, M. Kaya, and M. Hacıömeroğlu, “Classification of trashnet dataset based on deep learning models,” in 2018 IEEE International Conference on Big Data (Big Data), pp. 2058–2062, 2018.
[36] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 1800–1807, 2017.

Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Durmuş Özdemir ^*
0000-0002-9543-4076
Türkiye

Naciye Nur Arslan This is me
0000-0002-3208-7986
Türkiye

Publication Date

April 30, 2022

Submission Date

July 29, 2021

Acceptance Date

October 4, 2021

Published in Issue

Year 2022 Volume: 10 Number: 2

DOI

https://doi.org/10.29130/dubited.976118

IZ

https://izlik.org/JA22HX63CF

APA

Özdemir, D., & Arslan, N. N. (2022). Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images. Duzce University Journal of Science and Technology, 10(2), 628-640. https://doi.org/10.29130/dubited.976118

AMA

1.Özdemir D, Arslan NN. Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images. DUBİTED. 2022;10(2):628-640. doi:10.29130/dubited.976118

Chicago

Özdemir, Durmuş, and Naciye Nur Arslan. 2022. “Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease With Chest X-Ray Images”. Duzce University Journal of Science and Technology 10 (2): 628-40. https://doi.org/10.29130/dubited.976118.

EndNote

Özdemir D, Arslan NN (April 1, 2022) Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images. Duzce University Journal of Science and Technology 10 2 628–640.

IEEE

[1]D. Özdemir and N. N. Arslan, “Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images”, DUBİTED, vol. 10, no. 2, pp. 628–640, Apr. 2022, doi: 10.29130/dubited.976118.

ISNAD

Özdemir, Durmuş - Arslan, Naciye Nur. “Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease With Chest X-Ray Images”. Duzce University Journal of Science and Technology 10/2 (April 1, 2022): 628-640. https://doi.org/10.29130/dubited.976118.

JAMA

1.Özdemir D, Arslan NN. Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images. DUBİTED. 2022;10:628–640.

MLA

Özdemir, Durmuş, and Naciye Nur Arslan. “Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease With Chest X-Ray Images”. Duzce University Journal of Science and Technology, vol. 10, no. 2, Apr. 2022, pp. 628-40, doi:10.29130/dubited.976118.

Vancouver

1.Durmuş Özdemir, Naciye Nur Arslan. Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images. DUBİTED. 2022 Apr. 1;10(2):628-40. doi:10.29130/dubited.976118