Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images


  • N. Kumar Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, India
  • A. Hashmi Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, India
  • M. Gupta Department of Computer Science and Engineering, Moradabad Institute of Technology, India
  • A. Kundu Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, India
Volume: 12 | Issue: 1 | Pages: 7993-7997 | February 2022 |


Covid-19 is a highly infectious disease that spreads extremely fast and is transmitted through indirect or direct contact. The scientists have categorized the Covid-19 cases into five different types: severe, critical, asymptomatic, moderate, and mild. Up to May 2021 more than 133.2 million peoples have been infected and almost 2.9 million people have lost their lives from Covid-19. To diagnose Covid-19, practitioners use RT-PCR tests that suffer from many False Positive (FP) and False Negative (FN) results while they take a long time. One solution to this is the conduction of a greater number of tests simultaneously to improve the True Positive (TP) ratio. However, CT-scan and X-ray images can also be used for early detection of Covid-19 related pneumonia. By the use of modern deep learning techniques, accuracy of more than 95% can be achieved. We used eight CNN (CovNet)-based deep learning models, namely ResNet 152 v2, InceptionResNet v2, Xception, Inception v3, ResNet 50, NASNetLarge, DenseNet 201, and VGG 16 for both X-rays and CT-scans to diagnose pneumonia. The achieved comparative results show that the proposed models are able to differentiate the Covid-19 positive cases.


artificial intelligence, covid-19 detection, convolutional neural networks, deep learning


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"Weekly epidemiological update on COVID-19 - 6 April 2021," WHO. (accessed Dec. 06, 2021).

"Weekly operational update on COVID-19 - 7 December 2021," WHO. (accessed Dec. 07, 2021).

"COVID Live Update: 266,465,020 Cases and 5,275,134 Deaths from the Coronavirus - Worldometer." (accessed Dec. 06, 2021).

"Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)," WHO, Feb. 2020.

Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for Disease Control and Prevention, "The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China," Chinese Journal of Epidemiology, vol. 41, no. 2, pp. 145–151, 2020. DOI:

Z. Zhu et al., "Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort," Interdisciplinary Sciences: Computational Life Sciences, vol. 13, no. 1, pp. 73–82, Mar. 2021. DOI:

V. Madaan et al., "XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks," New Generation Computing, vol. 39, no. 3, pp. 583–597, Nov. 2021. DOI:

X. Xu et al., "A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia," Engineering, vol. 6, no. 10, pp. 1122–1129, Oct. 2020. DOI:

S. Wang et al., "A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)," European Radiology, vol. 31, no. 8, pp. 6096–6104, Aug. 2021. DOI:

S. Yadav, J. K. Sandhu, Y. Pathak, and S. Jadhav, "Chest X-ray scanning based detection of COVID-19 using deepconvolutional neural networ." 2020. DOI:

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, vol. 296, no. 2, pp. E41–E45, Aug. 2020. DOI:

Y. Fang, H. Zhang, Y. Xu, J. Xie, P. Pang, and W. Ji, "CT Manifestations of Two Cases of 2019 Novel Coronavirus (2019-nCoV) Pneumonia," Radiology, vol. 295, no. 1, pp. 208–209, Apr. 2020. DOI:

D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, "Pneumonia Detection Using CNN based Feature Extraction," in IEEE International Conference on Electrical, Computer and Communication Technologies, Coimbatore, India, Feb. 2019, pp. 1–7. DOI:

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, vol. 296, no. 2, pp. E65–E71, Aug. 2020. DOI:

O. Gozes et al., "Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis," arXiv:2003.05037 [cs, eess], Mar. 2020, [Online]. Available:

K. Purohit, A. Kesarwani, D. R. Kisku, and M. Dalui, "COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model," Oct. 2020. DOI:

B. Sekeroglu and I. Ozsahin, "Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks," SLAS TECHNOLOGY: Translating Life Sciences Innovation, vol. 25, no. 6, pp. 553–565, Dec. 2020. DOI:

K.-C. Liu et al., "CT manifestations of coronavirus disease-2019: A retrospective analysis of 73 cases by disease severity," European Journal of Radiology, vol. 126, May 2020, Art. no. 108941. DOI:

N. Kumar, M. Gupta, D. Gupta, and S. Tiwari, "Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images," Journal of ambient intelligence and humanized computing, pp. 1–10, May 2021. DOI:

M. Kaur, V. Kumar, V. Yadav, D. Singh, N. Kumar, and N. N. Das, "Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images," Journal of Healthcare Engineering, vol. 2021, Mar. 2021, Art. no. e8829829. DOI:

N. Narayan Das, N. Kumar, M. Kaur, V. Kumar, and D. Singh, "Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays," IRBM, Jul. 2020. DOI:

N. Kumar, N. Narayan Das, D. Gupta, K. Gupta, and J. Bindra, "Efficient Automated Disease Diagnosis Using Machine Learning Models," Journal of Healthcare Engineering, vol. 2021, May 2021, Art. no. e9983652. DOI:

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," Computers in Biology and Medicine, vol. 121, Jun. 2020, Art. no. 103792. DOI:

"COVID-19 Radiography Database." (accessed Dec. 06, 2021).

J. P. Cohen, ieee8023/covid-chestxray-dataset. 2021.

SARS-COV-2 "SARS-COV-2 Ct-Scan Dataset." (accessed Dec. 07, 2021).

"COVID-CT/Data-split at master • UCSD-AI4H/COVID-CT," GitHub. (accessed Dec. 07, 2021).

M. Rahimzadeh and A. Attar, "Detecting and Counting Pistachios based on Deep Learning," arXiv:2005.03990 [cs, eess], May 2020. DOI:

S. Ghosh, P. Shivakumara, P. Roy, U. Pal, and T. Lu, "Graphology based handwritten character analysis for human behaviour identification," CAAI Transactions on Intelligence Technology, vol. 5, no. 1, pp. 55–65, 2020. DOI:

J. Dekhtiar, A. Durupt, M. Bricogne, B. Eynard, H. Rowson, and D. Kiritsis, "Deep learning for big data applications in CAD and PLM – Research review, opportunities and case study," Computers in Industry, vol. 100, pp. 227–243, Sep. 2018. DOI:

H. A. Owida, A. Al-Ghraibah, and M. Altayeb, "Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7296–7301, Aug. 2021. DOI:

P. Chakraborty and C. Tharini, "Pneumonia and Eye Disease Detection using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5769–5774, Jun. 2020. DOI:

Y. Said, M. Barr, and H. E. Ahmed, "Design of a Face Recognition System based on Convolutional Neural Network (CNN)," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608–5612, Jun. 2020. DOI:

T. Guo, J. Dong, H. Li, and Y. Gao, "Simple convolutional neural network on image classification," in IEEE 2nd International Conference on Big Data Analysis, Beijing, China, Mar. 2017, pp. 721–724. DOI:

B. Gupta, M. Tiwari, and S. Singh Lamba, "Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement," CAAI Transactions on Intelligence Technology, vol. 4, no. 2, pp. 73–79, 2019. DOI:

K. D. Gupta, R. Dwivedi, and D. K. Sharma, “Prediction of Covid-19 trends in Europe using generalized regression neural network optimized by flower pollination algorithm,” Journal of Interdisciplinary Mathematics, vol. 24, no. 1, pp. 33–51, Jan. 2021. DOI:

D. N. N. Das, D. N. Kumar, D. Sharma, and S. Rao, “Covid19 – Transmission of Corona Virus and It’s Mathematical Model to Analyze,” Journal of Critical Reviews, vol. 7, no. 19, pp. 4779–4784, 2020.


How to Cite

N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, “Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 1, pp. 7993–7997, Feb. 2022.


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