| Peer-Reviewed

Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM

Received: 7 May 2021     Accepted: 24 May 2021     Published: 3 June 2021
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Abstract

To successfully contain the spread of COVID-19, the importance of swift and accurate testing is unparalleled. Currently, PCR tests are most commonly utilized to detect COVID-19, yet these tests typically consume 24 hours—not a short period of time. Hence, new deep learning algorithms have been under development to accurately and quickly detect COVID-19. With this aim, we have proposed a deep learning model to determine the presence of COVID-19 using X-ray images by combining Xception with Bi-LSTM. Altering the output from the Xception network into a three-dimensional shape rendered the ensuing Bi-LSTM network compatible. Consequently, the novel model yielded a high accuracy rate of 98.5%, one greater than the accuracy rates of VGG16, Densenet, Mobilenet, Mobilenet_v2, Resnet50, and DNN models. Moreover, with the creation of a heatmap, by using a Class Activation Map, our model could specifically locate the anomaly. However, our model could not yield high accuracy when we applied it to the lung ct scan dataset. Even though training and validation accuracy kept rising, the test accuracy was far lower than them. Furthermore, with limitations including a small sample size, inflated accuracy rates for binary classification, and incompatibility with CT images, follow-up research will need to ensue to perfect the model at hand.

Published in Advances in Bioscience and Bioengineering (Volume 9, Issue 2)
DOI 10.11648/j.abb.20210902.13
Page(s) 32-38
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

COVID-19, Chest X-ray, Xception, CNN, Bi-LSTM, CT-scan

References
[1] Coronavirus. (n. d.). https://www.who.int/health-topics/coronavirus#tab=tab_3.
[2] World Health Organization. (n. d.). WHO Coronavirus (COVID-19) Dashboard. World Health Organization. https://covid19.who.int/.
[3] Estimated transmissibility and impact OF SARS-CoV-2 Lineage B.1.1.7 in England. (2020, December 23). https://cmmid.github.io/topics/covid19/uk-novel-variant.html.
[4] Commissioner, O. of the. (n. d.). A Closer Look at COVID-19 Diagnostic Testing. U.S. Food and Drug Administration. https://www.fda.gov/health-professionals/closer-look-covid-19-diagnostic-testing.
[5] Mei, X., Lee, H.-C., Diao, K., Huang, M., Lin, B., Liu, C., … Yang, Y. (2020, April 17). Artificial intelligence-enabled rapid diagnosis of COVID-19 patients. medRxiv: the preprint server for health sciences. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274240/.
[6] 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
[7] Islam, M. Z., Islam, M. M., & Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in Medicine Unlocked, 20, 100412. https://doi.org/10.1016/j.imu.2020.100412
[8] Hassantabar, S., Ahmadi, M., & Sharifi, A. (2020). Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos, Solitons & Fractals, 140, 110170. https://doi.org/10.1016/j.chaos.2020.110170
[9] Khadidos, A., Khadidos, A. O., Kannan, S., Natarajan, Y., Mohanty, S. N., & Tsaramirsis, G. (2020). Analysis of COVID-19 Infections on a CT Image Using DeepSense Model. Frontiers in Public Health, 8. https://doi.org/10.3389/fpubh.2020.599550
[10] Ahuja, S., Panigrahi, B. K., Dey, N., Rajinikanth, V., & Gandhi, T. K. (2020). Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Applied Intelligence, 51 (1), 571–585. https://doi.org/10.1007/s10489-020-01826-w
[11] Bukhari, S. U., Bukhari, S. S., Syed, A., & Shah, S. S. (2020). The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19. https://doi.org/10.1101/2020.03.26.20044610
[12] Khoong, W. H. (2020, March 19). COVID-19 Xray Dataset (Train & Test Sets). Kaggle. https://www.kaggle.com/khoongweihao/covid19-xray-dataset-train-test-sets.
[13] LuisBlanche. (2020, April 9). COVID-19 Lung CT Scans. Kaggle. https://www.kaggle.com/luisblanche/covidct.raefs
[14] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9 (8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
[15] Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18 (5-6), 602-610. [doi: 10.1016/j.neunet.2005.06.042]
[16] Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.195
[17] Kwaśniewska, A., Rumiński, J., & Rad, P. (2017, July). Deep features class activation map for thermal face detection and tracking. In 2017 10Th international conference on human system interactions (HSI) (pp. 41-47). IEEE.
Cite This Article
  • APA Style

    Eric Dawson Oh, Jeong Gyoun Song, Yeon-Joon Jordan Kim, Wonse Kim. (2021). Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM. Advances in Bioscience and Bioengineering, 9(2), 32-38. https://doi.org/10.11648/j.abb.20210902.13

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    ACS Style

    Eric Dawson Oh; Jeong Gyoun Song; Yeon-Joon Jordan Kim; Wonse Kim. Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM. Adv. BioSci. Bioeng. 2021, 9(2), 32-38. doi: 10.11648/j.abb.20210902.13

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    AMA Style

    Eric Dawson Oh, Jeong Gyoun Song, Yeon-Joon Jordan Kim, Wonse Kim. Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM. Adv BioSci Bioeng. 2021;9(2):32-38. doi: 10.11648/j.abb.20210902.13

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  • @article{10.11648/j.abb.20210902.13,
      author = {Eric Dawson Oh and Jeong Gyoun Song and Yeon-Joon Jordan Kim and Wonse Kim},
      title = {Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM},
      journal = {Advances in Bioscience and Bioengineering},
      volume = {9},
      number = {2},
      pages = {32-38},
      doi = {10.11648/j.abb.20210902.13},
      url = {https://doi.org/10.11648/j.abb.20210902.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.abb.20210902.13},
      abstract = {To successfully contain the spread of COVID-19, the importance of swift and accurate testing is unparalleled. Currently, PCR tests are most commonly utilized to detect COVID-19, yet these tests typically consume 24 hours—not a short period of time. Hence, new deep learning algorithms have been under development to accurately and quickly detect COVID-19. With this aim, we have proposed a deep learning model to determine the presence of COVID-19 using X-ray images by combining Xception with Bi-LSTM. Altering the output from the Xception network into a three-dimensional shape rendered the ensuing Bi-LSTM network compatible. Consequently, the novel model yielded a high accuracy rate of 98.5%, one greater than the accuracy rates of VGG16, Densenet, Mobilenet, Mobilenet_v2, Resnet50, and DNN models. Moreover, with the creation of a heatmap, by using a Class Activation Map, our model could specifically locate the anomaly. However, our model could not yield high accuracy when we applied it to the lung ct scan dataset. Even though training and validation accuracy kept rising, the test accuracy was far lower than them. Furthermore, with limitations including a small sample size, inflated accuracy rates for binary classification, and incompatibility with CT images, follow-up research will need to ensue to perfect the model at hand.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM
    AU  - Eric Dawson Oh
    AU  - Jeong Gyoun Song
    AU  - Yeon-Joon Jordan Kim
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    T2  - Advances in Bioscience and Bioengineering
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    UR  - https://doi.org/10.11648/j.abb.20210902.13
    AB  - To successfully contain the spread of COVID-19, the importance of swift and accurate testing is unparalleled. Currently, PCR tests are most commonly utilized to detect COVID-19, yet these tests typically consume 24 hours—not a short period of time. Hence, new deep learning algorithms have been under development to accurately and quickly detect COVID-19. With this aim, we have proposed a deep learning model to determine the presence of COVID-19 using X-ray images by combining Xception with Bi-LSTM. Altering the output from the Xception network into a three-dimensional shape rendered the ensuing Bi-LSTM network compatible. Consequently, the novel model yielded a high accuracy rate of 98.5%, one greater than the accuracy rates of VGG16, Densenet, Mobilenet, Mobilenet_v2, Resnet50, and DNN models. Moreover, with the creation of a heatmap, by using a Class Activation Map, our model could specifically locate the anomaly. However, our model could not yield high accuracy when we applied it to the lung ct scan dataset. Even though training and validation accuracy kept rising, the test accuracy was far lower than them. Furthermore, with limitations including a small sample size, inflated accuracy rates for binary classification, and incompatibility with CT images, follow-up research will need to ensue to perfect the model at hand.
    VL  - 9
    IS  - 2
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Author Information
  • Yongsan International School of Seoul, Seoul, Republic of Korea

  • North London Collegiate School Jeju, Jeju, Republic of Korea

  • Seoul International School, Gyeonggi-do, Republic of Korea

  • St. Paul's School, New Hampshire, United States

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