Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks

Year 2021, Volume: 6 Issue: 1, 60 - 74, 25.01.2021

Yakup Kutlu Yunus Camgözlü

https://doi.org/10.28978/nesciences.868087

Cited By: 7

Abstract

COVID-19 is an epidemic disease that seriously affects elderly people and patients with chronic diseases and causes deaths. Fast and accurate early diagnosis has an important role. Although chest images obtained by computed tomography are accepted as a gold standard, problems are often encountered in accessing this device. For this reason, it is very important to diagnose with more accessible devices such as x-ray machines. These studies have been accelerated with deep neural network models and good results have been obtained. In this study, two different approach models are proposed for this purpose. At first study, training with the COVID-19 data set shared as open access and the test results with different classifiers. The other is the comparison of the results using a Pre-trained model MobileNet. COVID-19 patients, pneumonia patients and normal individuals were classified with 99.53% accuracy by the designed CNN with SVM model which was trained with the COVID-19 data set. As a result, because X-rays are a special type of image, a CNN model trained with X-ray images would be a good choice rather than using pre-trained deep networks with different images. As a result, since X-rays are a special type of picture, it was seen that a CNN model trained with X-ray images should be a better choice, rather than using pre-trained deep networks with different images.

Keywords

Diagnosis, COVID-19, Coronavirus, X-ray images, Deep learning, CNN

References

• Altan, G., & Kutlu, Y. (2018). Hessenberg Elm autoencoder kernel for deep learning. Journal of Engineering Technology and Applied Sciences, 3(2), 141-151.
• Altan, G., Kutlu, Y., & Allahverdi, N. (2019). Deep learning on computerized analysis of chronic obstructive pulmonary disease. IEEE Journal of Biomedical and Health Informatics, 24(5), 1344-1350.
• Altan, G., Kutlu, Y., Garbi, Y., Pekmezci, A. Ö., & Nural, S. (2017). Multimedia respiratory database (RespiratoryDatabase@ TR): Auscultation sounds and chest X-rays. Natural and Engineering Sciences, 2(3), 59-72.
• Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635-640.
• Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121, 103795.
• Badnjevic, A., Gurbeta, L., & Custovic, E. (2018). An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings. Scientific reports, 8(1), 1-9.
• Camgözlü, Y., & Kutlu, Y. (2020). Analysis of Filter Size Effect In Deep Learning. arXiv preprint arXiv:2101.01115.
• Chollet, F. (2018). Deep learning with Python (Vol. 361). New York: Manning.
• Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B.,... & Islam, M. T. (2020). Can AI help in screening viral and COVID-19 pneumonia?. IEEE Access, 8, 132665-132676.
• Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis,. New York: Wiley.
• Edgar Lorente, (2020). COVID-19 pneumonia-evolution over a week https://radiopaedia.org/cases/COVID-19-pneumonia-evolution-over-a-week-1?lang=us, version (2020). Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., ... & Ronneberger, O. (2019). U-Net: deep learning for cell counting, detection, and morphometry. Nature Methods, 16(1), 67-70.
• Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, 1-13.
• Ghoshal, B., & Tucker, A. (2020). Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. ArXiv preprint arXiv:2003.10769. Goodfellow I., Bengio Y., Courville A., (2006). Deep Learning, MIT Press.
• Gulli A., Pal S., Deep learning with Keras, Packt Publishing Ltd., 2017.
• Han, D., Liu, Q., & Fan, W. (2018). A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 95, 43-56.
• Hemdan, E. E. D., Shouman, M. A., & Karar, M. E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. ArXiv preprint arXiv:2003.11055.
• Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint ArXiv:1704.04861.
• Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
• Kanne, J. P., Little, B. P., Chung, J. H., Elicker, B. M., & Ketai, L. H. (2020). Essentials for radiologists on COVID-19: an update—radiology scientific expert panel. Khan, A. I., Shah, J. L., & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 196, 105581.
• Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
• Kutlu, Y., Yayik, A., Yildirim, E., & Yildirim, S. (2015, November). Orthogonal extreme learning machine based p300 visual event-related bci. In International Conference on Neural Information Processing (pp. 284-291). Springer, Cham.
• Kutlu, Y., Yayık, A., Yildirim, E., & Yildirim, S. (2019). LU triangularization extreme learning machine in EEG cognitive task classification. Neural Computing and Applications, 31(4), 1117-1126.
• Kutlu, Y. (2010). Multi-stage classification of abnormal patterns in EEG and e-ECG using model-free methods (Doctoral dissertation, DEÜ Fen Bilimleri Enstitüsü).
• Li, T., Han, Z., Wei, B., Zheng, Y., Hong, Y., & Cong, J. (2020). Robust screening of covid-19 from chest x-ray via discriminative cost-sensitive learning. arXiv preprint arXiv:2004.12592.
• Lin, D. T., Yan, C. R., & Chen, W. T. (2005). Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system. Computerized Medical Imaging and Graphics, 29(6), 447-458.
• Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020). CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Computers in Biology and Medicine, 122, 103869.
• Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint ArXiv:2003.10849.
• Nihashi, T., Ishigaki, T., Satake, H., Ito, S., Kaii, O., Mori, Y., ... & Naganawa, S. (2019). Monitoring of fatigue in radiologists during prolonged image interpretation using fNIRS. Japanese Journal of Radiology, 37(6), 437-448.
• Noble, W. S. (2006). What is a support vector machine?. Nature Biotechnology, 24(12), 1565-1567.
• Ozturk T., Talo M., Yildirim E.A., Baloglu U.B., Yildirim O., Acharya U.R., (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images, Computers in Biology and Medicine, 121, 103792.
• Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features. Preprints, 2020030300.
• Taylor-Phillips, S., & Stinton, C. (2019). Fatigue in radiology: a fertile area for future research. The British Journal of Radiology, 92(1099), 20190043.
• Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images. Medical Hypotheses, 109761.
• Vasilakos A.V., Tang Y., Yao Y., (2016). Neural networks for computer-aided diagnosis in medicine: a review, Neurocomputing, 216, 700-708.
• Wang L., Lin Z.Q., Wong A., (2020). COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images, Scientific Reports, 10, 19549.
• WHO - World Health Organization, (2020). Coronavirus disease (COVID-19) situation report of weekly operational, https://covid19.who.int/,version (2020).
• Wong T., Yang N., (2017). Dependency analysis of accuracy estimates in k-fold cross validation, IEEE Transactions on Knowledge and Data Engineering, 29, 2417-2427, 2017.
• Wong, H. Y. F., Lam, H. Y. S., Fong, A. H. T., Leung, S. T., Chin, T. W. Y., Lo, C. S. Y., ... & Ng, M. Y. (2020). Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology, 201160.
• Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020). Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing. Radiology, 200343.
• Zhang Y., (2019). Classification and diagnosis of thyroid carcinoma using reinforcement residual network with visual attention mechanisms in ultrasound images, Journal of Medical Systems, 43, 323.
• Zu, Z. Y., Jiang, M. D., Xu, P. P., Chen, W., Ni, Q. Q., Lu, G. M., & Zhang, L. J. (2020). Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology, 200490.