Skip to main content

Optimization of an Autonomous Learning Model for Detection COVID-19 Using Medical Images

  • 96 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 405)


Since COVID-19 appeared in 2019, detection methods based on medical images depend on the criteria of the specialist. This paper shows the optimization process of an autonomous image-based learning system. To improve the classification of positive images for COVID-19, obtained from a simple chest tomography, expert judgment and singular value decomposition are used. A computed tomography scan generates images in dicom format that must be converted to jpg or bmp format, so it is necessary to determine which format has better resolution. With the new processed images, matrix of size 145 × 145, a dataset is formed to train the neural network. When obtaining the neural network model, a reduction in training times between processed and unprocessed images is observed. Likewise, the predictions of the neural network, developed in python, for the detection of COVID-19 with processed images indicate results over 98%. Another important result is that singular value decomposition can be used to determine that images in bmp format provide more detail than images in jpg format, working with 90% of the original information in the image. Finally, the image classification is improved and the neural network is optimized.


  • Optimization
  • COVID-19
  • Neural network

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-96043-8_18
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   189.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-96043-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   249.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.


  1. Proaño, C.: On the macroeconomic and social impact of the coronavirus pandemic in Latin America and the developing world. Intereconomics 55, 159–162 (2020)

    CrossRef  Google Scholar 

  2. Sun, J., Aghemo, A., Forner, A., Valenti, L.: COVID-19 and liver disease. Liver Int. 40(6), 1278–1281 (2020)

    CrossRef  Google Scholar 

  3. Karaye, I., Horney, J.: The impact of social vulnerability on COVID-19 in the US: an analysis of spatially varying relationships. Am. J. Prev. Med. 59(3), 317–325 (2020)

    CrossRef  Google Scholar 

  4. Molina, N., Mejias, M.: Social impact of COVID-19 in Brazil and Ecuador: where reality surpasses the statistics. Edumecentro 12(3), 277–283 (2020)

    Google Scholar 

  5. Calvache, J., Rodríguez, A., Martínez, C., Paucar, V.: Usefulness of polymerase chain tests, rapid tests and CT scans in patients with Covid-19. J. Am. Health 3(2), 32–39 (2020)

    CrossRef  Google Scholar 

  6. Pareja, J., Anicama, S., Perez, P., Pecho, S., Amado, J.: Importance of the implementation of the chest tomography to contribute to the early diagnosis and timely triage of patients with COVID-19 in Peruvian hospitals. Acta Méd. Peruana 37(2), 239–241 (2020)

    Google Scholar 

  7. Pham, T.: A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks. Sci. Rep. 10(1), 1–8 (2020)

    CrossRef  Google Scholar 

  8. Gorina, A., Berenguer, I., Salgado, A., Álvarez, J.: The management of scientific information provided by the criteria of experts. Ciencias de la Información 45(2), 39–47 (2014)

    Google Scholar 

  9. Badii, M., Castillo, J., Guillen, A.: Optimal sample size. Innovaciones de negocios 5(9), 53–65 (2017)

    Google Scholar 

  10. Lyra, D., Carvalho, J., Azevedo, J.: Enhanced reconstruction of magnetic resonance data using singular value decomposition approximation. Ingeniería 17(2), 35–45 (2012)

    Google Scholar 

  11. MathWorks homepage.;jsessionid=7c0d111aa3913e4333866507c1e9. Accessed 21 Nov 2016

  12. Li, Y., Ma, W., Zhao, Y.: Application of digital image processing technology based on artificial intelligence in the analysis of medical images. Invest. Clin. 60(6), 1548–1561 (2019)

    Google Scholar 

  13. Smith, L.N.: A disciplined approach to neural network hyper-parameters: Part 1-learning rate, batch size, momentum, and weight decay, pp. 1–21. ArXiv, arXiv:1803.09820 (2018)

  14. Colab. Accessed 23 Mar 2021

  15. Singh, D., Kumar, V., Kaur, M.: Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. Eur. J. Clin. Microbiol. Infect. Dis. 39(7), 1379–1389 (2020)

    CrossRef  Google Scholar 

  16. Varela, S., Melin, P.: A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf. Sci. 545, 403–414 (2021)

    MathSciNet  CrossRef  Google Scholar 

  17. Mahmud, T., Rahman, M., Fattah, S.: 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. 122, 103869 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Rodrigo Bastidas-Chalán .

Editor information

Editors and Affiliations

Ethics declarations

Matlab and Python code used in this study is available in:

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Bastidas-Chalán, R., Medina, P. (2022). Optimization of an Autonomous Learning Model for Detection COVID-19 Using Medical Images. In: Botto-Tobar, M., Cruz, H., Díaz Cadena, A., Durakovic, B. (eds) Emerging Research in Intelligent Systems. CIT 2021. Lecture Notes in Networks and Systems, vol 405. Springer, Cham.

Download citation