Book Title: Contemporary Research Across Disciplines
Editors: Dr. R. Saravana Selvakumar and Mr. R. Venkatesan
ISBN: 978-81-978738-1-2
Chapter: 15
DOI: https://doi.org/10.59646/crc15/278
Authors:
Mrs. S. Sivasakthi, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore – 43, Tamil Nadu, India.
Dr V. Radha, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore-43, Tamil Nadu, India
Abstract
The current pandemic is a severe intimidation to human lives. Early detection and preliminary diagnosis are among the most important aspects of controlling the COVID-19 epidemic. The current clinical detection methods, such as Reverse Transcription Polymerase Chain Reaction (RT-PCR), involve collecting patient swab specimens. Although widely used and recognized, this method has some limitations regarding accuracy and testing time. As an alternative, Radiographic pictures have become an essential instrument for accomplishing this goal. This paper aims to present a summary of the deep learning methods created for COVID-19 identification using radiological data (X-ray and CT images). It also provides some insight into the techniques used in this discipline for feature extraction and data preprocessing. This work aims to facilitate researchers’ understanding of the several deep learning methods employed to identify COVID-19 and to present or combine those methods to stop the virus from spreading in the future.
References
Amyar, A., Modzelewski, R., Li, H., & Ruan, S. (2020). Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Computers in Biology and Medicine, 126, 104037.
Ardakani, A.A., Kanafi, A.R., Acharya, U.R., Khadem, N., & Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121, 103795.
Bernheim, X., Mei, X., Huang, M., Yang, Y., Fayad, Z.A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., et al. (2020). Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection. Radiology, 200463.
Buonsenso, D., Pata, D., & Chiaretti, A. (2020). COVID-19 outbreak: Less stethoscope, more ultrasound. The Lancet Respiratory Medicine, 8(5), e27.
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., & Yu, T. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. The Lancet, 395(10223), 507-513.
Ciaburro, G., & Venkateswaran, B. (2017). Neural networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing Ltd.
COVID-19 Data Set. (n.d.). Kaggle. Available: https://www.kaggle.com/sudalairajkumar/covid19-in-india/code
Forsman, P.M., et al. (2013). Efficient driver drowsiness detection at moderate levels of drowsiness. Accident Analysis & Prevention, 50, 341-350.
Gozes, O., Frid-Adar, M., Sagie, N., Zhang, H., Ji, W., & Greenspan, H. (2020). Coronavirus detection and analysis on chest CT with deep learning. arXiv preprint, arXiv:2004.02640.
Huang, Z., Liu, W., & Zhang, Q. (2020). Multi-modal fusion for COVID-19 detection: A multi-modal fusion approach for COVID-19 detection from chest X-rays and CT scans. Computers in Medicine, 126, 105037.
Li, Y., Wang, X., Gao, X., & Jiang, Q. (2020). Attention-based deep learning for COVID-19 detection: An attention-based deep learning approach for COVID-19 detection from chest X-rays. IEEE Transactions on Medical Imaging, 39(7), 1526-1536.
Priya, R. S., & Sriramalakshmi, A. (2024). People’s perception towards Ayurveda medicines after COVID-19. Journal of Inventive and Scientific Research Studies, Special Issue, 1-10.
Rajeswari, A. (2023). Automatic diagnosis of skin cancer using dermoscopic images by deep learning. Journal of Inventive and Scientific Research Studies, Vol. I, Issue 2, 191–201.
Rajpurkar, P., Irvin, J., Ball, R., Zhu, K., Yang, B., Mehta, H., & Ng, A.Y. (2017). Pneumonia detection using deep learning: A deep learning approach for pneumonia detection from chest X-rays. In Proceedings of the 2017 AAAI Conference on Artificial Intelligence.
Steffi, J., & Merriliance, K. (2024). An analysis of volumetric microscopy images for automatic cell segmentation using deep learning techniques. Journal of Inventive and Scientific Research Studies, Vol. II(Issue 1), 72-90.
Tang, T., Zhang, X., & Li, Z. (2020). Transfer learning for COVID-19 detection: A transfer learning approach for COVID-19 detection from chest X-rays. Journal of Medical Imaging and Health Informatics, 10(8), 2023-2030.
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). European Radiology, 1-9.
Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., & Tan, W. (2020). Detection of SARS-CoV-2 in different types of clinical specimens. Jama, 323(18), 1843-1844.
Wang, X., Xu, Z., Zhang, Z., & Gao, H. (2020). COVID-Net: A deep learning framework for COVID-19 detection from chest X-rays. IEEE Transactions on Neural Networks, 31(5), 1381-1389.
WHO, E.F. (2020). Novel Coronavirus (2019-nCoV) Situation Report—39. World Health Organization.
Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., & Lang, G. (2020). A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering, 6(10), 1122-1129.
Zhang, Y., Qiao, Q., & Li, C. (2020). Deep learning for pneumonia and COVID-19 detection: A deep learning framework for detecting pneumonia and COVID-19 from chest X-rays. Computerized Medical Imaging and Graphics, 89, 101889.
Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., & Dong, Y. (2021). The ensemble deep learning model for novel COVID-19 on CT images. Applied Soft Computing, 98, 106885.