Abstract
The dramatic rise of the novel coronavirus disease 2019 (COVID-19) pandemic has made it necessary to improve existing medical screening and clinical management of this disease diagnosis. The COVID-19 patients are known to exhibit a variety of symptoms, the major symptoms include fever, cough, and fatigue. Since these symptoms also appear in pneumonia patients, this creates complications in COVID-19 detection, especially during the flu season. Early studies identified abnormalities in chest X-ray images of COVID-19-infected patients that could be beneficial for disease diagnosis. Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep convolutional neural network (DCNN) architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. In this study, DCNN models cater to image classification. We used publicly available resources of 13,377 images and further strengthened our model by tuning hyperparameters to provide better generalization during the model validation phase. The experimental results have shown the overall accuracy as high as 97.5% which demonstrates the good capability of the proposed DCNN model in the current application domain. Comparative results in terms of accuracy, and error rate between the networks are presented. The excremental result show that our proposed methods is efficient and effective.
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
Cucinotta D, Vanelli M (2020) WHO declares COVID-19 a pandemic. Acta Biomedica Atenei Parmensis 91:157–160
Rustam F, Reshi AA, Mehmood A et al (2020) COVID-19 future forecasting using supervised machine learning models. IEEE Access
Cennimo DJ (2020) Coronavirus disease 2019 (COVID-19) clinical presentation, vol 8, pp 101489–101499. https://emedicine.medscape.com/article/2500114-clinical#b2. Online
Bressem KK, Adams LC, Albrecht J, Petersen A, Thieb HM, Niehues SM, Vahldiek JL (2020) Is lung density associated with severity of COVID-19? Pol J Radiol 85:e600-606
Cleverley J, Piper J, Jones MM (2020) The role of chest radiography in confirming COVID-19 pneumonia. BMJ 370:m2426
Rubin GD, Reyerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S et al (2020) The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner society. Chest 158:106–116
Annarumma M, Withey SJ, Bakewell RJ, Pesce E, Goh V, Montana G (2019) Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology 291(1):196–202
Mazurowski MA, Buda M, Saha A, Bashir MR (2019) Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging 49(4):939–954
Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Zeitschrift f¨ur Medizinische Physik 29(2):102–127
Ahmad M (2021) Ground truth labeling and samples selection for hyperspectral image classification. Optik 230. Article ID 166267
Kayalibay B, Jensen G, van der Smagt P (2017) CNN-based segmentation of medical imaging data. http://arxiv.org/abs/1701.03056
Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M (2014) Medical image classification with convolutional neural network. In: Proceedings of the 2014 13th international conference on control automation robotics & vision (ICARCV), pp 844–848, Singapore, December 2014
Umer M, Sadiq S, Ahmad M, Ullah S, Choi GS, Mehmood A (2020) A novel stacked CNN for malarial parasite detection in thin blood smear images. IEEE Access 8:93782–93792
Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 42(3):990–1002
Sharif M, Khan MA, Rashid M, Yasmin M, Afza F, Tanik UJ (2019) Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images. J Exp Theoret Artif Intell 1–23
Asada N, Doi K, MacMahon H et al (1990) Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: pilot study. Radiology 177(3):857–860
Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118
Dong Y, Pan Y, Zhang J, Xu W (2017) Learning to read chest X-ray images from 16000+ examples using CNN. In: Proceedings of the 2017 IEEE/ACM international conference on connected health: applications, systems and engineering technologies (CHASE), pp 51–57, Philadelphia, PA, USA, July 2017
Dong D, Tang Z, Wang S et al (2020) The role of imaging in the detection and management of COVID-19: a review. IEEE Rev Biomed Eng 14:16–19
Wang L, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. http://arxiv.org/abs/2003.09871
Abbas A, Abdelsamea MM, Gaber MM (2020) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. http://arxiv.org/abs/2003.13815
Reshi AA, Rustam F, Mehmood A, Alhossan A, Alrabiah Z, Ahmad A, Alsuwailem H, Choi (GS) An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Hindawi Complexity, vol 2021. Article ID 6621607, 12 pp. https://doi.org/10.1155/2021/6621607
Patel P (2020) Chest X-ray (COVID-19 & pneumonia). Kaggle. https://www.kaggle.com/prashant268/chest-xray-covid19-pneumonia
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
Ugli, O.O.O., Alam, M.N., Shirin, K.A., Al-Absi, A.A., Mannan, Z.I. (2022). COVID-19 X-Ray Image Classification Using Deep Convolution Neural Network. In: Pattnaik, P.K., Sain, M., Al-Absi, A.A. (eds) Proceedings of 2nd International Conference on Smart Computing and Cyber Security. SMARTCYBER 2021. Lecture Notes in Networks and Systems, vol 395. Springer, Singapore. https://doi.org/10.1007/978-981-16-9480-6_37
Download citation
DOI: https://doi.org/10.1007/978-981-16-9480-6_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9479-0
Online ISBN: 978-981-16-9480-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)