Performance Evaluation of the NASNet Convolutional Network in the Automatic Identification of COVID-19

Fredy Martinez, Fernando Martínez, Edwar Jacinto


This paper evaluates the performance of the Neural Architecture Search Network (NASNet) in the automatic detection of COVID-19 (Coronavirus Disease 2019) from chest x-ray images. COVID-19 is a disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that produces in patients fever, cough, shortness of breath, muscle pain, sputum production, diarrhea, and even sore throat. The virus spreads through the air, and to date, is expanding as a global pandemic. There is no vaccine, and it is fatal to approximately 2-7% of the infected population. Among the clinical and paraclinical characteristics of infected patients, nodules have been identified in images of chest x-rays that can be visually identified, producing a simple, rapid, and generally available method of identification. However, the rapid spread of the disease means that there is a lack of specialized medical personnel capable of identifying it, which is why automated schemes are being developed. We propose the tuning of a NASNet-type convolutional model to automatically determine the initial state of a patient in the triage process or intervention protocol of health care centers. The neural network is trained with public images of cases positively identified as patients infected with the virus and patients in normal conditions without infection. Performance evaluation is also done with real images unknown to the neuronal model. As for performance metrics, we use the function of loss of cross-entropy (categorical cross-entropy), the accuracy (or success rate), and the MSE (Mean Squared Error). The tuned model was able to correctly classify the test images with an accuracy of 97%.


convolutional neural network; COVID-19; data mining; diagnostic radiography; diseases; feature extraction; image classification; medical image processing; radiographs data mining; ROC chart.

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P. Masters, “Coronavirus genomic RNA packagingâ€, Virology, vol. 537, no. 1, pp. 198–207, 2019, ISSN 0042-6822, doi:

X. Xuanyang, G. Yuchang, W. Shouhong, and L. Xi, “Computer aided detection of SARS based on radiographs data miningâ€, IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 7459–7462, 2005, doi:10.1109/IEMBS.2005.1616237.

K. Das, E. Lee, R. Singh, M. Enani, K. Dossari, K. Gorkom, S. Larsson, and R. Langer, “Follow-up chest radiographic findings in patients with MERS-CoV after recoveryâ€, Indian Journal of Radiology and Imaging, vol. 27, no. 3, pp. 342–349, 2017, ISSN 0970-3016, doi:10.4103/ijri.IJRI_469_16.

M. Saad, A. Omrani, K. Baig, A. Bahloul, F. Elzein, M. Abdul, M. Selim, M. Mutairi, D. Nakhli, A. Aidaroos, N. Sherbeeni, H. Khashan, Z. Memish, and A. Albarrak, “Clinical aspects and outcomes of 70 patients with middle east respiratory syndrome coronavirus infection: a single-center experience in saudi arabiaâ€, International Journal of Infectious Diseases, vol. 29, no. 1, pp. 301–306, 2014, ISSN 1201-9712, doi:

E. Sabeti, J. Drews, N. Reamaroon, J. Gryak, M. Sjoding, and K. Najarian, “Detection of acute respiratory distress syndrome by incorporation of label uncertainty and partially available privileged informationâ€, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019), pp. 1717–1720, 2019, doi:10.1109/EMBC.2019.8857434.

F. Pan, T. Ye, P. Sun, S. Gui, B. Liang, L. Li, D. Zheng, J. Wang, R. Hesketh, L. Yang, and C. Zheng, “Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumoniaâ€, Radiology, vol. 295, no. 2, pp. 1–15, 2020, ISSN 0033-8419, doi:

F. Kofi, C. Dzuvor, M. Kormla, N. Bennita, and A. Habib, “Updates on Wuhan 2019 novel coronavirus epidemicâ€, Journal of Medical Virilogy, vol. 92, no. 4, pp. 403–407, 2020, ISSN 1096-9071, doi:10.1002/jmv.25695.

M. Malta, A. Rimoin, and S. Strathdee, “The coronavirus 2019-nCoV epidemic: Is hindsight 20/20?â€, EClinicalMedicine, vol. 20, no. 100289, pp. 1–2, 2020, ISSN 2589-5370, doi:

T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, and Z. S. andL. Xia, “Correlation of chest ct and rt-pcr testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 casesâ€, Radiology, vol. 295, no. 2, pp. 1–23, 2020, ISSN 0033-8419, doi:

Y. Fang, H. Zhang, J. Xie, M. Lin, L. Ying, P. Pang, and W. Ji, “Sensitivity of chest CT for COVID-19: comparison to RT-PCRâ€, Radiology, vol. 295, no. 2, pp. 1–8, 2020, ISSN 0033-8419, doi:

H. Bai, B. Hsieh, Z. Xiong, K. Halsey, J. Choi, T. Linh, I. Pan, D. Wang, J. Mei, X. Jiang, Q. Zeng, T. Egglin, P. Hu, S. Argarwal, F. Xie, S. Li, T. Healey, M. Atalay, and W. Liao, “Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CTâ€, Radiology, vol. 295, no. 2, pp. 1–28, 2020, ISSN 0033-8419, doi:

C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, X. Gu, Z. Cheng, T. Yu, J. Xia, Y. Wei, W. Wu, X. Xie, W. Yin, H. Li, M. Liu, Y. Xiao, H. Gao, L. Guo, J. Xie, G. Wang, R. Jiang, Z. Gao, and Q. Jin, “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, Chinaâ€, The Lancet Journal, vol. 395, no. 10223, pp. 497–506, 2020, ISSN 0140-6736, doi:

L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song, K. Cao, D. Liu, G. Wang, Q. Xu, X. Fang, S. Zhang, J. Xia, and J. Xia, “Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CTâ€, Radiology, vol. 295, no. 2, pp. 1–16, 2020, ISSN 0033-8419, doi:

M. Ng, Y. Lee, J. Yang, F. Yang, L. Xia, H. Wang, M. Lui, C. Lo, B. Leung, P. Khong, C. Hui, K. Yuen, and M. Kuo, “Imaging profile of the COVID-19 infection: Radiologic findings and literature reviewâ€, Radiology: Cardiothoracic imaging, vol. 2, no. 1, pp. 1–26, 2020, ISSN 2638-6135, doi:

N. Muller, C. Ooi, L. Pek, and N. Savvas, “Severe acute respiratory syndrome: Radiographic and CT findingsâ€, American Journal of Roentgenology, vol. 181, no. 1, pp. 3–8, 2003, ISSN 0361-803X, doi:10.2214/ajr.181.1.1810003.

K. Gostic, A. Gomez, R. Munnah, A. Kucharski, and J. Lloyd, “Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19â€, eLife, vol. 9, no. 1, pp. 1–18, 2020, ISSN 2050-084X, doi:10.7554/eLife.55570.

H. Rothan and S. Byrareddy, “The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreakâ€, Journal of Autoimmunity, vol. 109, no. 1, pp. 1–4, 2020, ISSN 0896-8411, doi:

K. Santosh, “AI-Driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal dataâ€, Journal of Medical Systems, vol. 44, no. 93, pp. 1–5, 2020, ISSN 0148-5598, doi:

O. Gozes, M. Frid-Adar, H. Greenspan, P. Browning, A. Bernheim, and E. Siegel, “Rapid ai development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysisâ€, arXiv, 2020, Eprint: 2003.05037v2.

X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, L. Yu, Y. Chen, J. Su, G. Lang, Y. Li, H. Zhao, K. Xu, L. Ruan, and W. Wu, “Deep learning system to screen coronavirus disease 2019 pneumoniaâ€, arXiv, 2020, Eprint: 2002.09334v1.

D. Kermany, M. Goldbaum, W. Cai, C. Valentim, H. Liang, S. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. Prasadha, J. Pei, M. Ting, J.Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. Huu, C. Wen, E. Zhang, C. Zhang, O. Li, X. Wang, M. Singer, X. Sun, J. Xu, A. Tafreshi, M. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learningâ€, Cell, vol. 172, no. 5, pp. 1122–1131, 2018, ISSN 0092-8674, doi:

F. Martínez, C. Hernández, and F. Martínez, “Evaluation of deep neural network architectures in the identification of bone fissuresâ€, Telkomnika, vol. 18, no. 2, pp. 807–814, 2020, ISSN 1693-6930, doi:10.12928/TELKOMNIKA.v18i2.14754.

K. Radhik, K. Devika, T. Aswathi, P. Sreevidya, V. Sowmya, and K. Soman, “Performance analysis of NASNet on unconstrained ear recognitionâ€, Nature Inspired Computing for Data Science, vol. 871, no. 1, pp. 57–82, 2019, ISSN 1860-949X, doi:

A. Rendón, F. Martínez, and C. Hernández, “Deep regression model for predictive control in a vegetable waste carbonization plantâ€, Contemporary Engineering Sciences, vol. 10, no. 21, pp. 1047–1055, 2017, ISSN 1314-7641, doi:

J. Cohen, “COVID-19 image data collectionâ€, On line, 2020,

P. Mooney, “Chest x-ray images (pneumonia)â€, On line, 2020,



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