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.
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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).
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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
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