Abstract
The global impact of the COVID-19 pandemic has created a significant health crisis affecting millions worldwide. Clinical symptom assessment and chest X-ray tomography are regularly consumed for diagnosing and monitoring COVID-19. To contribute to this effort, our research conducted a comparative analysis of various deep-learning (DL) models for categorizing chest X-ray images of pneumonia and COVID-19, introducing a novel model that outperforms existing ones. The pandemic has intensified the need for prompt diagnosis and treatment. Crucially, chest X-ray imaging has a fundamental role in identifying and tracking the progression of COVID-19. Evaluating our approach on a publicly available chest X-ray dataset, we achieved exceptional accuracy, sensitivity, and specificity rates of 95.7%, 94.3%, and 96.9%, respectively. These results underscore the skill of DL-based approaches in automated COVID-19 discovery from images of chest X-Ray tomography, facilitating swift and accurate diagnosis. Our research demonstrates the promising capacity of DL methods for rapid and precise identification of COVID-19 disease from X-ray images, offering valuable support for timely diagnosis of the condition.
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
Gorbalenya, A.E., Baker, S.C., Baric, R.S., et al.: The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-COVID-19. Nat. Microbiol. 5(5), 536–544 (2020)
Khan, S., Kazmi, A., Bashir, N., Siddique, R.: COVID-19 infection: emergence, transmission, and characteristics of human coronaviruses. J. Adv. Res. 24, 91–98 (2020)
World Health Organization: Director-General’s Opening Remarks at the Media Briefing on COVID. WHO, Geneva, Switzerland (2020). https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-atthe-media-briefing-on-COVID-19. Accessed 11 Mar 2020
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Codella, N.C., Nguyen, Q.B., Pankanti, S., Gutman, D.A., Helba, B., Halpern, A.C., et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4/5), 5–1 (2017)
Talo, M., Yildirim, O., Baloglu, U.B., Aydin, G., Acharya, U.R.: Convolutional neural networks for multi-class brain disease detection using MRI images. Comput. Med. Imaging Graph. 78, 101673 (2019)
Souza, J.C., Diniz, J.O.B., Ferreira, J.L., da Silva, G.L.F., Silva, A.C., de Paiva, A.C.: An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. Comput. Methods Prog. Biomed. 177, 285–296 (2019)
Akshaya, P.B.R.,Aravinda, C.V.: Predictive analysis of malignant disease in woman using machine learning techniques. In: Chiplunkar, N., Fukao, T. (eds) AIDE 2019. AISC, vol. 1133, pp. 431–438. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-3514-7_33
Yıldırım, Ö., Pławiak, P., Tan, R.S., Acharya, U.R.: Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput. Biol. Med. 102, 411–420 (2018)
Tan, J.H., Fujita, H., Sivaprasad, S., Bhandary, S.V., Rao, A.K., Chua, K.C., et al.: Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf. Sci. 420, 66–76 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, Las Vegas, NV, USA, December 2016
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA, September 2015
Naoto, N., et al.: Apathy classification based on doppler radar image for the elderly person. Front. Bioeng. Biotechnol. 8, 1235 (2020). https://www.frontiersin.org/article/10.3389/fbioe.2020.553847. https://doi.org/10.3389/fbioe.2020.553847, ISSN 2296-4185
Zhang, J.: Triple-view convolutional neural networks for COVID-19 diagnosis with chest x-ray (2020). http://arxiv.org/abs/2010.14091v1.
Ghoshal, B., Tucker, A.: Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection (2020). http://arxiv.org/abs/2003.10769
Meng, L., Lyu, B., Zhang, Z., Aravinda, C.V., Kamitoku, N., Yamazaki K.: Oracle bone inscription detector based on SSD. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11808. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30754-7_13
Aravinda, C.V., Lin, M., Atsumi, M., Udaya Kumar Reddy, K.R., Amar Prabhu, G.: A complete methodology for Kuzushiji historical character recognition using multiple features approach and deep learning model. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 11(8) (2020). https://doi.org/10.14569/IJACSA.2020.0110884
Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849 (2020)
Shi, H., Han, X., Jiang, N., Cao, Y., Alwalid, O., Gu J., et al.: Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect. Dis. (2020). pmid:32105637
Aravinda, C.V., Lin, M., Udaya Kumar Reddy, K.R., Amar Prabhu, G.: 23 - A demystifying convolutional neural networks using Grad-CAM for prediction of coronavirus disease (COVID-19) on X-ray images. In: Kose, U., Gupta, D., C. de Albuquerque, V.H., Khanna, A. (eds.) Data Science for COVID-19, pp. 429–450. Academic Press (2021). ISBN 9780128245361. https://doi.org/10.1016/B978-0-12-824536-1.00037-X. (https://www.sciencedirect.com/science/article/pii/B978012824536100037X)
Alharbi, A.H., Aravinda, C.V., Lin, M., Ashwini, B., Jabarulla, M.Y., Shah, M.A.: Detection of peripheral malarial parasites in blood smears using deep learning models. Comput. Intell. Neurosci. Article ID 3922763, 11 (2022). https://doi.org/10.1155/2022/3922763
ARAVINDA2022: A deep learning approach for the prediction of heart attacks based on data analysis. In: Deep Learning for Medical Applications with Unique Data, pp. 336–343. Academic Press (2022). 9780128241455. https://www.elsevier.com/books/deep-learning-for-medical-applications-with-unique-data/gupta/978-0-12-824145-5
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare no conflict of interest.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Aravinda, C.V., Sannidhan, M.S., Shetty, J., Shedthi, S., Bhatnagar, R. (2023). Comparing Different Deep Learning Models with a Novel Model for COVID-19 and Pneumonia Classification Using Chest X-Ray Images. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-43247-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43246-0
Online ISBN: 978-3-031-43247-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)