Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network


  • Salim M. Zaki Department of Computer Science, Dijlah University College (DUC), Baghdad, Iraq https://orcid.org/0000-0003-2995-3633
  • Mustafa Musa Jaber Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, 10021, Iraq & Al-Turath University College, Baghdad, Iraq https://orcid.org/0000-0002-5777-9428
  • Mohammed A. Kashmoola Department of Computer Science, College of Education, University of Al-Hamdaniya, Nineveh, Iraq https://orcid.org/0000-0003-3487-5908




COVID-19, Deep Learning, Image Processing, Neural Network, X-Ray Image Processing


With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  This research results showed that rapidly evolved Artificial Intelligence (AI) -based image analysis can accomplish high accuracy in detecting coronavirus infection as well as quantification and illness burden monitoring.


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Jiang F, Deng L, Zhang L, Cai Y, Cheung CW XZ. Review of the clinical characteristics of coronavirus disease 2019 (covid-19). J Gen Intern Med. 2020;1(5).

WHO. World Health Orgnization. Report: Q&As on COVID-19 and related health topics. Available online at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019, September 2020.

Sun D, Li H, Lu X-X, Xiao H, Ren J, Zhang F-R LZ-S. Clinical features of severe pediatric patients with coronavirus disease 2019 in wuhan: a single center’s observational study. World J Pediatr. 2020;1(9).

Ping An, Hongbin Chen, Xiaoda Jiang, Juan Su, Yong Xiao, Yijuan Ding, et al. Clinical Features of 2019 Novel Coronavirus Pneumonia Presented Gastrointestinal Symptoms But Without Fever Onset. http://dx.doi.org/102139/ssrn3532530. 2020

Al-Waisy AS, Mohammed1 MA, Al-Fahdawi S, Maashi MS, Garcia-Zapirain B, Abdulkareem KH, et al. COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images. Comput Mater Contin. 2020;67(2):2409–29.

Al-Waisy A S, Al-Fahdawi S , Mohammed MA. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Comput Springer. 2020.

Yicheng Fang, Huangqi Zhang, Jicheng Xie, Minjie Lin, Lingjun Ying, Peipei Pang WJ. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology. 2020;292(2):E115–7.

Xingzhi Xie, MD • Zheng Zhong, MD • Wei Zhao, MD • Chao Zheng, MD • Fei Wang, MD • Jun Liu M. Chest CT for Typical Coronavirus Disease 2019 (COVID19) Pneumonia: Relationship to Negative RT-PCR Testing. Radiology. 2020;296(2):E41–5.

Adam Bernheim , Xueyan Mei, Mingqian Huang, Yang Yang, Zahi A Fayad, Ning Zhang, et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology. 2020;295(2):685–691.

Lih OS, Jahmunah V, San TR, Ciaccio EJ, Yamakawa T, Tanabe M KM, et al. Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med. 2020;103:101789.

Wang X, Qian H, Ciaccio EJ, Lewis SK, Bhagat G, Green PH, et al. Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction. Comput Methods Programs Biomed. 2020;187:105236.

Dekhtiar J, Durupt A, Bricogne M, Eynard B, Rowson H KD. Deep learning for big data applications in CAD and PLM – Research review, opportunities and case study. Comput Ind. 2018;100:227–43.

Zaid Khalaf Hussien BND. Anomaly Detection Approach Based on Deep Neural Network and Dropout. Baghdad Sci J [Internet]. 2020;17: 701-709.

Hussain BA HM. Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection. Baghdad Sci J [Internet]. 2020;17(3): 909-915.

Emily Chen, Kristina Lerman EF. Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Heal Surveill. 2020;6(2):e1927:1–12.

Joseph Paul Cohen, Paul Morrison LD. COVID-19 Image Data Collection. arXiv Prepr 200311597. 2020;1–4.

Bai Y, Yao L, Wei T, Tian F, Jin D-Y, Chen L, et al. Presumed Asymptomatic Carrier Transmission of COVID-19. JAMA [Internet]. 2020 Apr 14;323(14):1406–7. https://doi.org/10.1001/jama.2020.2565

Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27:861–874.

Powers Dmw. Evaluation: From Precision, Recall And F-Measure To Roc, Informedness, Markedness & Correlation. J Mach Learn Technol [Internet]. 2011;2(1):37–63. Available from: http://www.bioinfo.in/contents.php?id=51

Gönen M. Analyzing Receiver Operating Characteristic Curves with SAS. J Biopharm Stat. 2008;18(6):1228–9.