3,163
Views
3
CrossRef citations to date
0
Altmetric
BIOMEDICAL ENGINEERING

Deep learning model for detection of COVID-19 utilizing the chest X-ray images

, ORCID Icon, & | (Reviewing editor)
Article: 2079221 | Received 30 Aug 2021, Accepted 27 Apr 2022, Published online: 29 May 2022

References

  • 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), 635–18. https://doi.org/10.1007/s13246-020-00865-4
  • Asif, S., & Wenhui, Y. (2020). Automatic detection of COVID-19 using X-ray images with deep convolutional neural networks and machine learning. medRxiv.
  • Asif, S., Wenhui, Y., Jin, H., & Jinhai, S. (2020, December). Classification of COVID-19 from chest X-ray images using deep convolutional neural network. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC), (pp. 426–433). IEEE.
  • Bai, H. X., Hsieh, B., Xiong, Z., Halsey, K., Choi, J. W., Tran, T. M. L., … Liao, W. H. (2020). Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology, 296(2), E46–E54. https://doi.org/10.1148/radiol.2020200823
  • Chadaga, K., Prabhu, S., Vivekananda, B. K., Niranjana, S., Umakanth, S., & Pham, D. T. (2021). Battling COVID-19 using machine learning: A review. Cogent Engineering, 8(1), 1958666. https://doi.org/10.1080/23311916.2021.1958666
  • Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., & De Albuquerque, V. H. C. (2020). A novel transfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences, 10(2), 559. https://doi.org/10.3390/app10020559
  • Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., Reaz, M. B. I., & Islam, M. T. (2020). Can AI help in screening viral and COVID-19 pneumonia? IEEE Access, 8, 132665–132676. https://doi.org/10.1109/ACCESS.2020.3010287
  • Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K., Bleicker, T., Brünink, S., Schneider, J., Schmidt, M. L., Mulders, D. G., Haagmans, B. L., van der Veer, B., van den Brink, S., Wijsman, L., Goderski, G., Romette, J.-L., Ellis, J., Zambon, M., … Drosten, C. (2020). Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance, 25(3), 2000045. https://doi.org/10.2807/1560-7917.ES.2020.25.3.2000045
  • Degerli, A., Ahishali, M., Yamac, M., Kiranyaz, S., Chowdhury, M. E., Hameed, K., Hamid, T., Mazhar, R., & Gabbouj, M. (2021). COVID-19 infection map generation and detection from chest X-ray images. Health Information Science and Systems, 9(1), 1–16. https://doi.org/10.1007/s13755-021-00146-8
  • Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). Sensitivity of chest CT for COVID-19: Comparison to RT-PCR. Radiology, 296(2), E115–E117. https://doi.org/10.1148/radiol.2020200432
  • Farooq, M., & Hafeez, A. (2020). Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003. 14395.
  • Giridhar, A., & Sampathila, N. (2021, March). Application of artificial intelligence to predict the degradation of potential mRNA vaccines developed to treat SARS-CoV-2. In International Conference on Machine Learning and Big Data Analytics, (pp. 85–94). Springer, Cham.
  • Goldstein, E., Keidar, D., Yaron, D., Shachar, Y., Blass, A., Charbinsky, L., … Eldar, Y. C. (2020). Covid-19 classification of x-ray images using deep neural networks. arXiv preprint arXiv:2010. 01362.
  • Hall, L. O., Paul, R., Goldgof, D. B., & Goldgof, G. M. (2020). Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:2004. 02060.
  • Han, X., Fan, Y., Alwalid, O., Li, N., Jia, X., Yuan, M., Li, Y., Cao, Y., Gu, J., Wu, H., & Shi, H. (2021). Six-month follow-up chest CT findings after severe COVID-19 pneumonia. Radiology, 299(1), E177–E186. https://doi.org/10.1148/radiol.2021203153
  • Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Turkbey, E. B., Yang, D., Myronenko, A., Anderson, V., Amalou, A., Blain, M., Kassin, M., Long, D., Varble, N., Walker, S. M., Bagci, U., Ierardi, A. M., Stellato, E., … Turkbey, B. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications, 11(1), 1–7. https://doi.org/10.1038/s41467-020-17971-2
  • Ismael, A. M., & Şengür, A. (2021). Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications, 164, 114054. https://doi.org/10.1016/j.eswa.2020.114054
  • Jain, G., Mittal, D., Thakur, D., & Mittal, M. K. (2020). A deep learning approach to detect Covid-19 coronavirus with X-ray images. Biocybernetics and Biomedical Engineering, 40(4), 1391–1405. https://doi.org/10.1016/j.bbe.2020.08.008
  • Kaggle, https://www.kaggle.com/tawsifurrahman
  • Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., Dong, J., Prasadha, M. K., Pei, J., Ting, M. Y. L., Zhu, J., Li, C., Hewett, S., Dong, J., Ziyar, I., … Zhang, K. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122–1131. https://doi.org/10.1016/j.cell.2018.02.010
  • Kesim, E., Dokur, Z., & Olmez, T. (2019, April). X-ray chest image classification by a small-sized convolutional neural network. In 2019 scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT), (pp. 1–5). IEEE.
  • 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. https://doi.org/10.1016/j.cmpb.2020.105581
  • Khandekar, R., Shastry, P., Jaishankar, S., Faust, O., & Sampathila, N. (2021). Automated blast cell detection for acute lymphoblastic leukemia diagnosis. Biomedical Signal Processing and Control, 68, 102690, . https://doi.org/10.1016/j.bspc.2021.102690
  • Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574–582. https://doi.org/10.1148/radiol.2017162326
  • LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010, May). Convolutional networks and applications in vision. In Proceedings of 2010 IEEE international symposium on circuits and systems, (pp. 253–256). IEEE.
  • Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., … Xia, J. (2020). Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology.
  • Li, B., Deng, A., Li, K., Hu, Y., Li, Z., Xiong, Q., … Lu, J. (2021). Viral infection and transmission in a large well-traced outbreak caused by the Delta SARS-CoV-2 variant. MedRxiv.
  • Maghded, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., Rawat, D. B., & Rabie, K. (2020, August). A novel AI-enabled framework to diagnose coronavirus COVID-19 using smartphone embedded sensors: Design study. In 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), (pp. 180–187). IEEE.
  • Maghdid, H. S., Asaad, A. T., Ghafoor, K. Z., Sadiq, A. S., Mirjalili, S., & Khan, M. K. (2021, April). Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In Multimodal Image Exploitation and Learning 2021 (Vol. 11734, pp. 117340E). International Society for Optics and Photonics.
  • Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Soufi, G. J. (2020). Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning. Medical Image Analysis, 65, 101794. https://doi.org/10.1016/j.media.2020.101794
  • Narin, A., Kaya, C., & Pamuk, Z. (2021). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 1–14. https://doi.org/10.1007/s10044-021-00984-y
  • 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. https://doi.org/10.1016/j.compbiomed.2020.103792
  • Piccinini, G. (2004). The First computational theory of mind and brain: A close look at mcculloch and Pitts' “logical calculus of ideas immanent in nervous activity”. Synthese, 141(2), 175–215. https://doi.org/10.1023/B:SYNT.0000043018.52445.3e
  • Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S. B. A., Islam, M. T., Al Maadeed, S., Zughaier, S. M., Khan, M. S., & Chowdhury, M. E. (2021). Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Computers in Biology and Medicine, 132, 104319. https://doi.org/10.1016/j.compbiomed.2021.104319
  • Rajaraman, S., Siegelman, J., Alderson, P. O., Folio, L. S., Folio, L. R., & Antani, S. K. (2020). Iteratively pruned deep learning ensembles for COVID-19 detection in chest X-rays. IEEE Access, 8, 115041–115050. https://doi.org/10.1109/ACCESS.2020.3003810
  • Salehi, S., Abedi, A., Balakrishnan, S., & Gholamrezanezhad, A. (2020). Coronavirus disease 2019 (COVID-19): A systematic review of imaging findings in 919 patients. American Journal of Roentgenology, 215(1), 87–93. https://doi.org/10.2214/AJR.20.23034
  • Sampathila, N., & Martis, R. J. (2020). Computational approach for content‐based image retrieval of K‐similar images from brain MR image database. Expert Systems, e12652. https://doi.org/10.1111/exsy.12652
  • Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features.
  • Shah, S., Mulahuwaish, A., Ghafoor, K. Z., & Maghdid, H. S. (2022). Prediction of global spread of COVID-19 pandemic: A review and research challenges. Artificial Intelligence Review, 55(3), 1607–1628. https://doi.org/10.1007/s10462-021-09988-w
  • Shi, H., Han, X., Jiang, N., Cao, Y., Alwalid, O., Gu, J., Fan, Y., & Zheng, C. (2020). Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: A descriptive study. The Lancet Infectious Diseases, 20(4), 425–434. https://doi.org/10.1016/S1473-3099(20)30086-4
  • Tabik, S., Gómez-Ríos, A., Martín-Rodríguez, J. L., Sevillano-García, I., Rey-Area, M., Charte, D., Guirado, E., Suarez, J. L., Luengo, J., Valero-Gonzalez, M. A., Garcia-Villanova, P., Olmedo-Sanchez, E., & Herrera, F. (2020). COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images. IEEE Journal of Biomedical and Health Informatics, 24(12), 3595–3605. https://doi.org/10.1109/JBHI.2020.3037127
  • Tahamtan, A., & Ardebili, A. (2020). Real-time RT-PCR in COVID-19 detection: Issues affecting the results. Expert Review of Molecular Diagnostics, 20(5), 453–454. https://doi.org/10.1080/14737159.2020.1757437
  • Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312. https://doi.org/10.1109/TMI.2016.2535302
  • Turkoglu, M. (2021). COVID-19 detection system using chest CT images and multiple kernels-extreme learning machine based on deep neural network. IRBM, 42(4), 207–214. https://doi.org/10.1016/j.irbm.2021.01.004
  • Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 2097–2106).
  • Watson, J., Whiting, P. F., & Brush, J. E. (2020). Interpreting a covid-19 test result. BMJ (Clinical Research Ed.), 369, m1808. https://doi.org/10.1136/bmj.m1808
  • Wikramaratna, P., Paton, R. S., Ghafari, M., & Lourenco, J. (2020). Estimating false-negative detection rate of SARS-CoV-2 by RT-PCR. MedRxiv.
  • World Health Organization (WHO), https://www.who.int
  • Worldometers. https://www.worldometers.info/coronavirus/