Skip to main content

Transfer Learning for Arthropodous Identification and its Use in the Transmitted Disease Diagnostic

  • Conference paper
  • First Online:
Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection (PAAMS 2021)

Abstract

Outdoors’s activities and sporadic nature getaways are becoming more and more common in recent years. Warm and humid climates without extreme temperatures favor insects or small organisms to live (and proliferate), which can cause potentially serious health problems if we do not have a minimum knowledge of what to do if we are bitten or stung. One of such concerning animals are the arthropodous. The objective of this work is to provide doctors and patients a machine learning-based tool to obtain a fast initial diagnostic based on a picture of the specimen which bit them. The developed model achieved over a 93% accuracy score based on a dataset of 493 color images. Three species have been categorized and analyzed, and the possible diseases they may transmit identified. The proposed system is effective and useful for a future real-life integration into a platform.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Masi, I., Wu, Y., Hassner, T., Natarajan, P.: Deep face recognition: a survey. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 471–478. IEEE October 2018

    Google Scholar 

  2. Mutz, I.: Las infecciones emergentes transmitidas por garrapatas. Annales Nestlé (Ed. española) 67(3), 123–134 (2009). https://doi.org/10.1159/000287275

    Article  Google Scholar 

  3. Steere, A.C., Coburn, J., Glickstein, L.: The emergence of Lyme disease. J. Clin. Investig. 113(8), 1093–1101 (2004)

    Article  Google Scholar 

  4. Bratton, R.L., Corey, G.R.: Tick-borne disease. Am. Fam. Phys. 71(12), 2323–2330 (2005)

    Google Scholar 

  5. de Castro, J.J.: Sustainable tick and tickborne disease control in livestock improvement in developing countries. Vet. Parasitol. 71(2–3), 77–97 (1997)

    Article  Google Scholar 

  6. Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016). https://doi.org/10.1186/s40537-016-0043-6

    Article  Google Scholar 

  7. Pan, S.J.: Transfer learning. Learning 21, 1–2 (2020)

    Google Scholar 

  8. Gutiérrez, S., Hernández, I., Ceballos, S., Barrio, I., Díez-Navajas, A.M., Tardaguila, J.: Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions. Comput. Electron. Agric. 182, 105991 (2021)

    Article  Google Scholar 

  9. Bjerge, K., Frigaard, C.E., Mikkelsen, P.H., Nielsen, T.H., Misbih, M., Kryger, P.: A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony. Comput. Electron. Agric. 164, 104898 (2019)

    Article  Google Scholar 

Download references

Acknowledgments

This research has been supported by the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGE-Mobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security”, Reference: RTI2018–095390-B-C32, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER).

The research was partially supported by the project “Computación cuántica, virtualización de red, edge computing y registro distribuido para la inteligencia artificial del futuro”, Reference: CCTT3/20/SA/0001, financed by Institute for Business Competitiveness of Castilla y León, and the European Regional Development Fund (FEDER).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Garcia-Retuerta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Garcia-Retuerta, D., Casado-Vara, R., Rodríguez, S. (2021). Transfer Learning for Arthropodous Identification and its Use in the Transmitted Disease Diagnostic. In: De La Prieta, F., El Bolock, A., Durães, D., Carneiro, J., Lopes, F., Julian, V. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. PAAMS 2021. Communications in Computer and Information Science, vol 1472. Springer, Cham. https://doi.org/10.1007/978-3-030-85710-3_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85710-3_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85709-7

  • Online ISBN: 978-3-030-85710-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics