Browse > Article
http://dx.doi.org/10.33851/JMIS.2021.8.4.251

Lightweight Convolutional Neural Network (CNN) based COVID-19 Detection using X-ray Images  

Khan, Muneeb A. (Department of Software, Sangmyung University)
Park, Hemin (Department of Software, Sangmyung University)
Publication Information
Journal of Multimedia Information System / v.8, no.4, 2021 , pp. 251-258 More about this Journal
Abstract
In 2019, a novel coronavirus (COVID-19) outbreak started in China and spread all over the world. The countries went into lockdown and closed their borders to minimize the spread of the virus. Shortage of testing kits and trained clinicians, motivate researchers and computer scientists to look for ways to automatically diagnose the COVID-19 patient using X-ray and ease the burden on the healthcare system. In recent years, multiple frameworks are presented but most of them are trained on a very small dataset which makes clinicians adamant to use it. In this paper, we have presented a lightweight deep learning base automatic COVID-19 detection system. We trained our model on more than 22,000 dataset X-ray samples. The proposed model achieved an overall accuracy of 96.88% with a sensitivity of 91.55%.
Keywords
Coronavirus; Computer Tomography; Convolutional Neural Network; X-ray;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Liang, Y. Liu, M. Wu, F. Garcia-Castro, A. Alberich-Bayarri and F. Wu, "Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice," Clinical Radiology, vol. 75, no. 1, pp. 38-45, 2020. Available: 10.1016/j.crad.2019.08.005.   DOI
2 H. Rathore, A. Al-Ali, A. Mohamed, X. Du and M. Guizani, "A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants," IEEE Access, vol. 7, pp. 24154-24164, 2019. Available: 10.1109/access.2019.2899558.   DOI
3 B. Mukherjee, "Fight Detection in Hockey Videos using Deep Network," Koreascience.or.kr, 2021. [Online]. Available: http://koreascience.or.kr/article/JAKO201707851608120.page.
4 N. Ghassemi, A. Shoeibi and M. Rouhani, "Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images," Biomedical Signal Processing and Control, vol. 57, p. 101678, 2020. Available: 10.1016/j.bspc.2019.101678.   DOI
5 "Summary of probable SARS cases with onset of illness from 1 November 2002 to 31 July 2003," Who.int, 2015. [Online]. Available:https://www.who.int/publications/m/item/summary-of-probable-sars-cases-with-onsetof-illness-from-1-november-2002-to-31-july2003. [Accessed: 04- Dec- 2021].
6 Beck, B. Shin, Y. Choi, S. Park and K. Kang, "Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARSCoV-2) through a drug-target interaction deep learning model," Computational and Structural Biotechnology Journal, vol. 18, pp. 784-790, 2020. Available: 10.1016/j.csbj.2020.03.025.   DOI
7 Y.-H. Heo, B.-G. Kim and P. P. Roy, "Frontal Face Generation Algorithm from Multi-view Images Based on Generative Adversarial Network," Journal of Multimedia Information System, vol. 8, no. 2, pp. 85-92, 2021. Available: 10.33851/jmis.2021.8.2.85.   DOI
8 Al-shamasneh and U. Obaidellah, "Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study," European Scientific Journal, vol. 13, no. 3, 2017. Available: 10.19044/esj.2016.v13n3p342.
9 A. Narin, C. Kaya and Z. Pamuk, "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks," Pattern Analysis and Applications, vol. 24, no. 3, pp. 1207-1220, 2021. Available: 10.1007/s10044-021-00984-y.   DOI
10 Wang, Y. Sun, T. Duong, L. Nguyen and L. Hanzo, "Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach," IEEE Access, vol. 8, pp. 115655-115661, 2020. Available: 10.1109/access.2020.3003750.   DOI
11 "Coronavirus," Who.int, 2021. [Online]. Available: https://www.who.int/health-topics/coronavirus. [Accessed: 04- Dec- 2021].
12 "Coronavirus disease (COVID-19) - World Health Organization," Who.int, 2021. [Online]. Available: https://www.who.int/emergencies/diseases/novelcoronavirus-2019. [Accessed: 04- Dec- 2021].
13 Bangare, "Brain Tumor Detection Using Machine Learning Approach," Design Engineering, no. 7, pp. 7557-7566, 2021. Available: http://www.thedesignengineering.com/index.php/DE/article/view/3264.
14 Y. Minoda et al., "Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors," Journal of Gastroenterology, vol. 55, no. 12, pp. 1119-1126, 2020. Available: 10.1007/s00535-020-01725-4.   DOI
15 X. Chen, L. Yao and Y. Zhang, "Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images," arXiv.org, 2020. [Online]. Available: https://arxiv.org/abs/2004.05645.
16 D. Singh, V. Kumar, Vaishali and M. Kaur, "Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks," European Journal of Clinical Microbiology & Infectious Diseases, vol. 39, no. 7, pp. 1379-1389, 2020. Available: 10.1007/s10096-020-03901-z.   DOI
17 B. Ghoshal, A. Tucker, "Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection," arXiv preprint arXiv:2003.10769. 2020 Mar 22.
18 M. Karar, S. El-Khafif and M. El-Brawany, "Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree," Journal of Medical Systems, vol. 41, no. 4, 2017. Available: 10.1007/s10916-017-0704-9.   DOI
19 S. Hussein, P. Kandel, C. Bolan, M. Wallace and U. Bagci, "Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1777-1787, 2019. Available: 10.1109/tmi.2019.2894349.   DOI
20 S. Wang et al., "A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)," European Radiology, 2021. Available: 10.1007/s00330-021-07715-1.   DOI
21 Hyo-Eun Kim, et al., "Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study," The Lancet Digital Health, vol. 2, no. 3, pp. e138-e148, 2020. Available: 10.1016/s2589-7500(20)30003-0.   DOI
22 "Human Resources - Doctors," Organization for Economic Co-operation and Development (OECD), 2021. [Online]. Available: https://data.oecd.org/healthres/doctors.html. [Accessed: 04- Dec- 2021].
23 El-Shafai, Walid; Abd El-Samie, Fathi (2020), "Extensive COVID-19 X-Ray and CT Chest Images Dataset," Mendeley Data, V3, doi: 10.17632/8h65ywd2jr.3   DOI
24 Ezz El-Din Hemdan, M. Shouman and M. Karar, "COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images," arXiv.org, 2020. [Online]. Available: https://arxiv.org/abs/2003.11055.
25 J.-H. Kim, B.-G. Kim, P. P. Roy and D.-M. Jeong, "Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure," IEEE Access, vol. 7, pp. 41273-41285, 2019. Available: 10.1109/access.2019.2907327.   DOI
26 "Middle East respiratory syndrome coronavirus (MERS-CoV)," Who.int, 2019. [Online]. Available: https://www.who.int/healthtopics/middle-east-respiratory-syndromecoronavirus-mers#tab=tab_1. [Accessed: 04- Dec2021].
27 P. Decharatanachart, R. Chaiteerakij, T. Tiyarattanachai and S. Treeprasertsuk, "Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis," BMC Gastroenterology, vol. 21, no. 1, 2021. Available: 10.1186/s12876-020-01585-5.   DOI
28 M. Karar, D. Merk, C. Chalopin, T. Walther, V. Falk and O. Burgert, "Aortic valve prosthesis tracking for transapical aortic valve implantation," International Journal of Computer Assisted Radiology and Surgery, vol. 6, no. 5, pp. 583-590, 2010. Available: 10.1007/s11548-010-0533-5.   DOI
29 N. C. Das Adhikari, "Infection Severity Detection of CoVID19 from X-Rays and CT Scans Using Artificial Intelligence," International Journal of Computer, vol. 38, no. 1, pp. 73-92, May 2020.