Menu
COVID-19 also known as Coronavirus is a global pandemic which has already affected millions of people around the world and is continuing to do so even now. Due to the high positivity rate among patients, the health and medical facilities faced lot of issues such as, lack of medical staff, beds, and intensive care units (ICUs). Therefore, it is important to identify and detect COVID-19 as earliest as possible. Over the years, a large number of methods were proposed for predicting covid-19, but the problem with those methods was that they were high complex and took huge time for training. In this paper, a bidirectional Long Short-Term memory (Bi-LSTM) based model is proposed that performs two classification tasks i.e. detection of COVID-19 and predicting the need for regular wards and ICU and semi-ICUs. The main objective of the proposed work is not only to decrease the complexity and training time of the model but also to enhance the accuracy of the system. For this, a publicly available dataset taken from the Kaggle.com is taken and pre-processing is applied to it, for making the data balanced and normalized. In addition to this, the complexity of the system is decreased by using the Eigenvector centrality feature selection (ECFS), in which only important and crucial features are selected. Furthermore, the effectiveness of the suggested system is enhanced by using the DL based classifier, named as, Bi-LSTM. Finally, the efficacy of the suggested approach is analyzed and compared with several traditional approaches in terms of various dependency factors like, accuracy, specificity, precision, recall and Fscore. The simulated outcomes determine the supremacy of the proposed approach over traditional approaches.
[1] S. Shaikh, J. Gala, A. Jain, S. Advani, S. Jaidhara and M. Roja Edinburgh, “Analysis and Prediction of COVID-19 using Regression Models and Time Series Forecasting,” 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2021, pp. 989-995, doi: 10.1109/Confluence51648.2021.9377137.
[2] R. Punia, L. Kumar, M. Mujahid and R. Rohilla, “Computer Vision and Radiology for COVID-19 Detection,” 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1-5.
[3] M. Rohini, K. R. Naveena, G. Jothipriya, S.Kameshwaran and M. Jagadeeswari, “A Comparative Approach To Predict Corona Virus Using Machine Learning,” 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 331-337, doi: 10.1109/ICAIS50930.2021.9395827.
[4] Wikipedia contributors. “COVID-19.” Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 23 Sep. 2021. Web. 25 Sep. 2021.
[5] Lupia, T.; Scabini, S.; Mornese Pinna, S.; Di Perri, G.; De Rosa, F.G.; Corcione, S. 2019 novel coronavirus (2019-nCoV) outbreak: A new challenge. J. Glob. Antimicrob. Resist. 2020, 21, 22-27.
[6] Yang, Y.; Peng, F.; Wang, R.; Guan, K.; Jiang, T.; Xu, G.; Sun, J.; Chang, C. The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. J. Autoimmun. 2020.
[7] Shinde V, Bhikha S, Hoosain Z, Archary M, Bhorat Q, Fairlie L, Lalloo U, Masilela MSL, Moodley D, Hanley S, Fouche L, Louw C, Tameris M, Singh N, Goga A, Dheda K, Grobbelaar C, Kruger G, Carrim-Ganey N, Baillie V, de Oliveira T, Lombard Koen A, Lombaard JJ, Mngqibisa R, Bhorat AE, Benadé G, Lalloo N, Pitsi A, Vollgraaff PL, Luabeya A, Esmail A, Petrick FG, Oommen-Jose A, Foulkes S, Ahmed K, Thombrayil A, Fries L, Cloney-Clark S, Zhu M, Bennett C, Albert G, Faust E, Plested JS, Robertson A, Neal S, Cho I, Glenn GM, Dubovsky F, Madhi SA., 2019nCoV-501 Study Group. Efficacy of NVX-CoV2373 Covid-19 Vaccine against the B.1.351 Variant. N Engl J Med. 2021 May 20;384(20):1899-1909.
[8] F. Shi et al., ‘’Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19,’’ IEEE Rev. Biomed. Eng., vol. 14, pp. 4-15, Jan. 2021.
[9] R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, ‘’Artificial intelligence (AI) applications for COVID-19 pandemic,’’ Diabetes Metabolic Syndrome, Clin. Res. Rev., vol. 14, no. 4, pp. 337-339, Jul. 2020
[10] A. F. M. Batista, J. L. Miraglia, T. H. R. Donato, A. D. P. C. Filho, “COVID-19 diagnosis prediction in emergency care patients: a machine learning approach”, medRxiv, 2020.
[11] M. Seviand ?. AYDIN, “COVID-19 Detection Using Deep Learning Methods,” 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020, pp. 1-6, doi: 10.1109/ICDABI51230.2020.9325626.
[12] S. Lafraxo and M. el Ansari, “CoviNet: Automated COVID-19 Detection from X-rays using Deep Learning Techniques,” 2020 6th IEEE Congress on Information Science and Technology (CiSt), 2020, pp. 489-494, doi: 10.1109/CiSt49399.2021.9357250.
[13] Xiaoshuo Li, Wenjun Tan, Pan Liu, Qinghua Zhou, Jinzhu Yang, “Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning”, Journal of Healthcare Engineering, vol. 2021, Article ID 5528441, 7 pages, 2021.
[14] Luca Brunese, Fabio Martinelli, Francesco Mercaldo, AntonellaSantone, Machine learning for coronavirus covid-19 detection from chest x-rays, Procedia Computer Science, Volume 176, 2020, Pages 2212-2221.
[15] SoumyajitPodder, SomnathBhattacharjee, Arijit Roy. An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images[J]. AIMS Biophysics, 2021, 8(3): 281-290.
[16] S. Sakib, T. Tazrin, M. M. Fouda, Z. M. Fadlullah and M. Guizani, “DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach,” in IEEE Access, vol. 8, pp. 171575-171589, 2020
[17] A. Channa, N. Popescu and N. u. R. Malik, “Robust Technique to Detect COVID-19 using Chest X-ray Images,” 2020 International Conference on e-Health and Bioengineering (EHB), 2020, pp. 1-6, doi: 10.1109/ EHB50910.2020.9280216.
[18] E. F. Ohata et al., “Automatic detection of COVID-19 infection using chest X-ray images through transfer learning,” in IEEE/CAA Journal of AutomaticaSinica, vol. 8, no. 1, pp. 239-248, January 2021, doi:10.1109/JAS.2020.1003393.
[19] Sethy, P.K.; Behera, S.K. Detection of Coronavirus Disease (COVID-19) Based on Deep Features. Preprints 2020, 2020030300 (doi: 10.20944/preprints202003.0300.v1).
[20] S K T Hwa1, A Bade1 and M H Ahmad Hijazi, Enhanced Canny edge detection for Covid-19 and pneumonia X-Ray images, IOP Conference Series: Materials Science and Engineering, Volume 979, International Conference on Virtual and Mixed Reality Interfaces 2020, 16-17 November 2020 16-17
Desh Bhagat University,
Off to NH-44, Amloh Road, Mandi Gobindgarh,
District Fatehgarh Sahib, Punjab- 147301, India