Covid-19 Detection Using AI

Authors

  • Shashank Mishra  Department of Computer Science and Engineering Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh , India
  • Himanshu Kumar Shukla  Department of Computer Science and Engineering Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh , India
  • Rajiv Singh  Department of Computer Science and Engineering Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh , India
  • Vivek Pandey  Department of Computer Science and Engineering Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh , India
  • Shubham Sagar  Department of Computer Science and Engineering Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh , India
  • Yasasvi Singh  Department of Computer Science and Engineering Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh , India

DOI:

https://doi.org//10.32628/IJSRSET2183130

Keywords:

Deep learning, CNN Algorithm Chest X-Ray Neural Network

Abstract

The sudden increase in COVID-19 patients is a major shock to our global health care systems. With limited availability of test kits, it is not possible for all patients with respiratory infections to be tested using RT-PCR. Testing also takes a long time, with limited sensitivity. The detection of COVID-19 infections on Chest X-Ray can help isolate patients at high risk while awaiting test results. X-Ray machines are already available in many health care systems, and with many modern X-Ray systems already installed on the computer, there is no travel time involved in the samples. In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. This can be useful in a hospital setting where current systems have difficulty deciding whether to keep the patient in the ward with other patients or isolate them from COVID-19 areas. It may also be helpful in identifying patients with high risk of COVID with false positive RT-PCR that will require repeated testing. In addition, we recommend the use of modern AI techniques to detect COVID-19 patients who use X-Ray imaging in an automated manner, especially in areas where radiologists are not available, and help make the proposed diagnostic technology easier. Introducing the CovidAID: COVID-19 AI Detector, a model based on a deep neural network of screening patients for proper diagnosis. In a publicly available covid-chest x-ray-dataset [2], our model provides 90.5% accuracy with 100% sensitivity (remember) to COVID-19 infection. We are greatly improving the results of Covid-Net [10] on the same database.

References

  1. Bennett, J.M.: Smart ct scan based covid19 virus detector. https: //github.com/JordanMicahBennett/SMART-CT-SCAN BASED-COVID19 VIRUS DETECTOR (2020)
  2. Cohen, J.P., Morrison, P., Dao, L.: Covid-19 image data collection. arXiv 2003.11597 (2020), https://github.com/ieee8023/covid-chestxray-dataset
  3. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
  4. Mooney, P.: Kaggle chest x-ray images (pneumonia) dataset. https://www.kaggle. com/paultimothymooney/chest-xray-pneumonia (2018)
  5. Nisar, Z.: https://github.com/zeeshannisar/covid-19. https://github.com/ zeeshannisar/COVID-19 (2020)
  6. Petsiuk, V., Das, A., Saenko, K.: Rise: Randomized input sampling for explanation of black-box models. In: Proceedings of the British Machine Vision Conference (BMVC) (2018)
  7. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., et al.: Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
  8. Ranjan, E., Paul, S., Kapoor, S., Kar, A., Sethuraman, R., Sheet, D.: Jointly learning convolutional representations to compress radiological images and classify thoracic diseases in the compressed domain (12 2018). https://doi.org/10.1145/3293353.3293408
  9. Ruiz, P.: Understanding and visualizing densenets. https://towardsdatascience. com/understanding-and-visualizing-densenets-7f688092391a (2018)
  10. Wang, L., Wong, A.: Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images (2020)
  11. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: 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 (2017)
  12. Weng, X., Zhuang, N., Tian, J., Liu, Y.: Chexnet for classification and localization of thoracic diseases. https://github.com/arnoweng/CheXNet/ (2017) 13. Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K.: Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501(2017)

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shashank Mishra, Himanshu Kumar Shukla, Rajiv Singh, Vivek Pandey, Shubham Sagar, Yasasvi Singh, " Covid-19 Detection Using AI, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 3, pp.561-566, May-June-2021. Available at doi : https://doi.org/10.32628/IJSRSET2183130