Abstract
COVID-19 has become the global epidemic affecting millions of people across the world. The fact that COVID-19 spreads quickly and devastating for elderly person makes this disease lethal as we witnessed a massive mortality rate in first, second, and third wave since 2020. Early diagnosis of COVID-19 is mandatory to prevent the spread and damage control, as only few nations have been able to vaccinate more than 50% of their population. The healthcare professionals commonly use the real-time polymerase chain reaction (RT-PCR) test to identify the COVID-19. Although RT-PCR test is considered more reliable among other COVID-19 detection tests; however, sensitivity of RT-PCR lies in the range of 65%-95% and took hours to diagnose the COVID-19 disease. Therefore, there exists an urgent need to develop more rapid and reliable diagnostics methods for COVID-19. In this regard, Chest X-ray and CT scan images are also being used to determine the abnormalities in the lungs of the COVID-19 patients which are found after the initial symptoms of this disease. We exploit the benefits of convolution neural network (CNN) for reliable detection of various diseases and used it for COVID-19 detection. For this purpose, we proposed a deep learning model to automatically detect the COVID-19 disease by processing the chest X-ray images. More specifically, we presented an Inception-ResNetV2 network-based deep learning model for COVID-19 detection. Performance of our model is evaluated on the publicly available COVID-19 dataset. The accuracy of 96% indicates the effectiveness of the proposed model for COVID-19 detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
WHO 2021 WHO. [Online] Available at: https://covid19.who.int/. Accessed 2021
Long C et al (2020) Diagnosis of the coronavirus disease (COVID-19): rRT-PCR or CT? Eur J Radiol 126:108961. https://doi.org/10.1016/j.ejrad.2020.108961
Radiologyassistant (2021) RadiologyAssistaant. [Online]. Available at: https://radiologyassistant.nl/chest/covid-19/covid19-imaging-findings. Accessed 2021
Ge H et al (2020) The epidemiology and clinical information about COVID-19. Nat Pub Health Emerg Collect 39:1011–1019. https://doi.org/10.1007/s10096-020-03874-z
Kong W, Agarwal PP (2020) Chest imaging appearance of COVID-19 infection. Radiol: Cardiothoracic Imaging 2(1). https://doi.org/10.1148/ryct.2020200028
Zhao W et al (2020) Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study. Am J Roentgenol (AJR) 214(5):1072–1077. https://doi.org/10.2214/AJR.20.22976
Kim H et al (2020) Diagnostic performance of CT and reverse transcriptase-polymerase chain reaction for coronavirus disease 2019: a meta-analysis. Radiol Soc North Am: Radiol 296(3). https://doi.org/10.1148/radiol.2020201343
Wang L et al (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. Sci Rep 10:19549. https://doi.org/10.1038/s41598-020-76550-z
Majeed T et al (2020) COVID-19 detection using CNN transfer learning from X-ray Images. medRxiv preprint. https://doi.org/10.1101/2020.05.12.20098954
Sethy PK, Behera SK (2020) Detection of coronavirus disease (COVID-19) based on deep features. Preprints 2020, 2020030300
Rehman A et al (2020) Improving coronavirus (COVID-19) diagnosis using deep transfer learning. medRxiv. https://doi.org/10.1101/2020.04.11.20054643
Zheng C et al (2020) Deep learning-based detection for COVID-19 from chest CT using weak label. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2020.2995965
Ghoshal B, Tucker A (2020) Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv, eess. arXiv:2003.10769
Apostolopoulos ID, MPesiana TA (2020) COVID-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks.. Phys Eng Sci Med. https://doi.org/10.1007/s13246-020-00865-4
Alam N-A-A, Ahsan M et al (2021) COVID-19 detection from chest X-ray images using feature fusion and deep learning. https://doi.org/10.3390/s21041480
Szegedy C et al (2017) Inception-v4, Inception-ResNet and the impact of residual connections on learning. arxiv journal arXiv:1602.07261v2
Elhamraoui Z (2020) Medium. [Online] Available at: https://medium.com/@zahraelhamraoui1997/inceptionresnetv2-simple-introduction-9a2000edcdb6
Cohen DJ (2020) github. [Online] Available at: https://github.com/ieee8023/covid-chestxray-dataset. Accessed Oct 2020
Mooney P (2020) Kaggle. [Online] Available at: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Accessed Oct 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Riaz, T., Dar, T., Ilyaas, H., Javed, A. (2022). An Inception-ResNetV2 Based Deep Learning Model for COVID-19 Detection. In: Ullah, A., Anwar, S., Rocha, Á., Gill, S. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 350. Springer, Singapore. https://doi.org/10.1007/978-981-16-7618-5_19
Download citation
DOI: https://doi.org/10.1007/978-981-16-7618-5_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7617-8
Online ISBN: 978-981-16-7618-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)