Low-risk covid-19 detection using a novel deep learning network
Main Article Content
Keywords
Computer Science, Artificial Intelligence, Deep Learning, Chest Radiographs, COVID-19, Convolutional Neural Network
Abstract
Computer Science is a field of deep knowledge, of which Artificial Intelligence has an imminent role in unraveling solutions to complex real-life situations. With technological advancements, we have been able to accomplish many said to be impossible tasks in the past, and are striving to achieve more and more everyday. Artificial Intelligence, being a sophisticated and advanced field of study, has revealed many techniques which can be used in various sectors of our society. One of such vital sectors is healthcare. Amidst many epidemics and pandemics, our human race has survived countless decades. Artificial Intelligence and its disciplines have proven to aid human beings when it comes to a healthy well-being. Among various disciplines, deep learning has assisted us in the most notable manner. In the current scenario where we are struggling to overcome a global pandemic, deep learning has led us to a breakthrough, which could possibly help us all in a positive manner. The proposed network utilizes deep learning to construct a feasible solution to detect COVID-19 with the help of CXR images(Chest Radiographs). COVID-19, being a respiratory disease, is said to accumulate in the lungs of the patients, and hence we can detect the virus’ presence at an early stage with this methodology. Our novel deep learning network is procured from the traditional Convolutional Neural Network, the working of which is similar to that of the structure of a human’s brain. Neural networks are said to function like a brain, analyzing and computing at faster rates than most of the existing networks. This network processes the image dataset consisting of chest radiographs to detect the presence of COVID-19 virus with the help of a novel neural networking model, which is said to have higher accuracy than the existing models.
References
2. P. D. Brown and S. A. Lerner, “Community-acquired pneumonia,” The Lancet, vol. 352, no. 9136, pp. 1295–1302, 1998.
3. M. Farooq and A. Hafeez, “COVID-ResNet: A deep learning framework for screening of COVID19 from radiographs,” 2020, arXiv:2003.14395. [Online]. Available: http://arxiv.org/abs/2003.14395
4. Franquet, T. Imaging of pneumonia: Trends and algorithms. Eur. Respir. J. 2001, 18, 196–208.
5. Pranav Rajpurkar * 1 Jeremy Irvin * 1 Kaylie Zhu 1 Brandon Yang 1 Hershel Mehta 1 Tony Duan 1 Daisy Ding 1 Aarti Bagul 1 Robyn L. Ball 2 Curtis Langlotz 3 Katie Shpanskaya 3 Matthew P. Lungren 3 Andrew Y. Ng, “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning”Computer Vision and Pattern Recognition, 25 Dec 2017,
6. Art no.1711.05225
7. Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017 Aug;284(2):574-582.doi:10.1148/radiol.2017162326. Epub 2017 Apr 24. PMID: 28436741.
8. H.Y. F. Wong et al., “Frequency and distribution of chest radiographic findings in COVID-19 positive patients,” Radiology, Mar. 2020, Art. no. 201160. 1. Y. Oh, S. Park and J. C. Ye, "Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets," in IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp.2688-2700, Aug. 2020, doi: 10.1109/TMI.2020.2993291.
9. Kallianos, K.; Mongan, J.; Antani, S.; Henry, T.; Taylor, A.; Abuya, J.; Kohli, M. How far have we come? Artificial intelligence for chest radiograph interpretation. Clin. Radiol. 2019, 74, 338–345.
10. Hosny, A.; Parmar, C.; Quackenbush, J.; Schwartz, L.H.; Aerts, H.J. Artificial intelligence in radiology. Nat. Rev. Cancer 2018, 18, 500–510.
11. Wang, Y.; Wang, C.; Zhang, H. Ship classification in high-resolution SAR images using deep learning of small datasets. Sensors 2018, 18, 2929.
12. S. Roy et al., "Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound," in IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2676-2687, Aug. 2020, doi: 10.1109/TMI.2020.2994459.
13. MDPI and ACS Style Luján-García, J.E.; Yáñez-Márquez, C.; Villuendas-Rey, Y.; Camacho-Nieto, O. A Transfer Learning Method for Pneumonia Classification and Visualization. Appl. Sci. 2020, 10, 2908.
14. Udugama B, Kadhiresan P, Kozlowski HN, et al. Diagnosing COVID-19: The Disease and Tools for Detection. ACS Nano. 2020;14(4):3822-3835. doi:10.1021/acsnano.0c02624.
15. Cocklin, M., Gourlay, A., Jackson, P., Kaye, G., Miessler, M., Kerr, I., & Lams, P. (1984). An image processing system for digital chest X-ray images. Computer Programs in Biomedicine, 19(1), 3–11. doi:10.1016/0010-468x(84)90033-3.
16. Prokop, M., Neitzel, U., & Schaefer-Prokop, C. (2003). Principles of Image Processing in Digital Chest Radiography. Journal of Thoracic Imaging, 18(3),148–164. doi:10.1097/00005382-200307000-00004
17. X. Ouyang et al., "Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia," in IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2595-2605, Aug. 2020, doi: 10.1109/TMI.2020.2995508.X. Wang et al.,
18. "A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT," in IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2615-2625, Aug. 2020, doi: 10.1109/TMI.2020.2995965.
19. Eisenstadt, Marc & Ramachandran, Manoharan & Chowdhury, Niaz & Third, Allan & Domingue, John. (2020). COVID-19 Antibody Test Vaccination Certification: There's an app for that. 1. 148-155. 10.1109/OJEMB.2020.2999214.
20. M. Jamshidi et al., "Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment," in IEEE Access, vol. 8, pp.109581-109595,2020,doi:10.1109/ACCESS.2020.3001973.
21. S. Hu et al., "Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images," in IEEE Access, vol. 8, pp. 118869-118883,2020, doi:10.1109/ACCESS.2020.3005510.
22. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105. https://doi.org/10.1145/3065386
23. Jason Brownlee(2019) Image Classification, Object Detection, and Face Recognition in Python in Deep Learning for Computer Vision, Machine Learning Mastery, 2019.
24. Ciresan, Dan; Ueli Meier; Jonathan Masci; Luca M. Gambardella; Jurgen Schmidhuber(2011). "Flexible, High Performance Convolutional Neural Networks for Image Classification"(PDF). Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence-Volume . 2:1237–1242. Retrieved 17 November 2013.
25. F Chollet. (2017). Xception: Deep Learning with Depthwise Separable Convolutions in IEEE Conference on Computer Vision and Pattern Recognition.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.