ModCOVNN: a convolutional neural network approach in COVID-19 prognosis

(1) * Ahmed Wasif Reza Mail (Department of Computer Science and Engineering, East West University, Bangladesh)
(2) Jannatul Ferdous Sorna Mail (Department of Computer Science and Engineering, East West University, Bangladesh)
(3) Md. Momtaz Uddin Rashel Mail (Department of Computer Science and Engineering, East West University, Bangladesh)
(4) Mir Moynuddin Ahmed Shibly Mail (Department of Computer Science and Engineering, East West University, Bangladesh)
*corresponding author


COVID-19 is a devastating pandemic in the history of humankind. It is a highly contagious flu that can spread from human to human. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. However, identifying COVID-19 patients with a Polymerase chain reaction (PCR) test can sometimes be problematic and time-consuming. Therefore, detecting patients with this virus from X-ray chest images can be a perfect alternative to the de-facto standard PCR test. This article aims at providing such a decision support system that can detect COVID-19 patients with the help of X-ray images. To do that, a novel convolutional neural network (CNN) based architecture, namely ModCOVNN, has been introduced. To determine whether the proposed model works with good efficiency, two CNN-based architectures – VGG16 and VGG19 have been developed for the detection task. The experimental results of this study have proved that the proposed architecture has outperformed the other two models with 98.08% accuracy, 98.14% precision, and 98.4% recall. This result indicates that proper detection of COVID-19 patients with the help of X-ray images of the chest is possible using machine learning methods with high accuracy. This type of data-driven system can help us to overcome the current appalling situation throughout the world.


COVID19; convolutional neural network; biomedical image processing; computer vision; X-ray



Article metrics

Abstract views : 329 | PDF views : 60




Full Text



[1] A. I. Khan, J. L. Shah, and M. M. Bhat, "CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images," Comput. Methods Programs Biomed., vol. 196, 2020, doi: 10.1016/j.cmpb.2020.105581.

[2] "Rolling updates on coronavirus disease (COVID-19)." Available at:

[3] K. Tolksdorf, S. Buda, E. Schuler, L. H. Wieler, and W. Haas, "Influenza-associated pneumonia as reference to assess seriousness of coronavirus disease (COVID-19)," Eurosurveillance, 2020, doi: 10.2807/1560-7917.ES.2020.25.11.2000258.

[4] G. Grasselli, A. Pesenti, and M. Cecconi, "Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy," JAMA, 2020, doi: 10.1001/jama.2020.4031.

[5] T. Mahmud, M. A. Rahman, and S. A. Fattah, "CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization," Comput. Biol. Med., 2020, doi: 10.1016/j.compbiomed.2020.103869.

[6] H. A. Rothan and S. N. Byrareddy, "The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak," Journal of Autoimmunity. 2020, doi: 10.1016/j.jaut.2020.102433.

[7] C. C. Lai, T. P. Shih, W. C. Ko, H. J. Tang, and P. R. Hsueh, "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges," International Journal of Antimicrobial Agents. 2020, doi: 10.1016/j.ijantimicag.2020.105924.

[8] E. Mahase, "Coronavirus covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate," BMJ, 2020, doi: 10.1136/bmj.m641.

[9] W. Guan et al., "Clinical Characteristics of Coronavirus Disease 2019 in China," N. Engl. J. Med., 2020, doi: 10.1056/nejmoa2002032.

[10] R. Punia, L. Kumar, M. Mujahid, and R. Rohilla, "Computer vision and radiology for COVID-19 detection," 2020, doi: 10.1109/INCET49848.2020.9154088.

[11] H. V Huff and A. Singh, "Asymptomatic Transmission During the Coronavirus Disease 2019 Pandemic and Implications for Public Health Strategies," Clin. Infect. Dis., 2020, doi: 10.1093/cid/ciaa654.

[12] X. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang, and J. Liu, "Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing," Radiology, 2020, doi: 10.1148/radiol.2020200343.

[13] A. Abbas, M. M. Abdelsamea, and M. M. Gaber, "Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network," Appl. Intell., 2020, doi: 10.1007/s10489-020-01829-7.

[14] L. Wang, Z. Q. Lin, and A. Wong, "COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images," Sci. Rep., 2020, doi: 10.1038/s41598-020-76550-z.

[15] I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Phys. Eng. Sci. Med., 2020, doi: 10.1007/s13246-020-00865-4.

[16] A. Narin, C. Kaya, and Z. Pamuk, "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks," arXiv. 2020. doi: 10.1007/s10044-021-00984-y

[17] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images," Comput. Biol. Med., 2020, doi: 10.1016/j.compbiomed.2020.103792.

[18] X. Xu et al., "A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia," Engineering, 2020, doi: 10.1016/j.eng.2020.04.010.

[19] O. Stephen, M. Sain, U. J. Maduh, and D. U. Jeong, "An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare," J. Healthc. Eng., 2019, doi: 10.1155/2019/4180949.

[20] P. K. Sethy, S. K. Behera, P. K. Ratha, and P. Biswas, "Detection of coronavirus disease (COVID-19) based on deep features and support vector machine," Int. J. Math. Eng. Manag. Sci., 2020, doi: 10.33889/IJMEMS.2020.5.4.052.

[21] E. E. D. Hemdan, M. A. Shouman, and M. E. Karar, "COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images," arXiv. 2020. Available at:

[22] L. Li et al., "Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy," Radiology, 2020, doi: 10.1148/radiol.2020200905.

[23] X. Xu et al., "Deep learning system to screen coronavirus disease 2019 pneumonia," arXiv. 2020. doi: 10.1016/j.eng.2020.04.010

[24] L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone, "Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays," Comput. Methods Programs Biomed., 2020, doi: 10.1016/j.cmpb.2020.105608.

[25] K. Simonyan and A. Zisserman, "VGG-16," arXiv Prepr., 2014. Available at: Google Scholar.

[26] A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro, "CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection," IEEE Access, 2020, doi: 10.1109/ACCESS.2020.2994762.

[27] K. He, X. Zhang, S. Ren, and J. Sun, “ResNet,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016. Available at: Google Scholar.

[28] C. Szegedy et al., "Going deeper with convolutions," 2015, doi: 10.1109/CVPR.2015.7298594.

[29] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-v4, inception-ResNet and the impact of residual connections on learning," 2017. Available at: Google Scholar.

[30] F. Chollet, "Xception: Deep learning with depthwise separable convolutions," 2017, doi: 10.1109/CVPR.2017.195.

[31] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," 2017, doi: 10.1109/CVPR.2017.243.

[32] M. Lin, Q. Chen, and S. Yan, "Network in network," 2014. Available at: Google Scholar.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan, and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
E: (paper handling issues), (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0