Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae).

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Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Identifying disease vector images in the Americas in the age of artificial intelligence.Revista da Sociedade Brasileira de Medicina Tropical · 2025
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5 · Who and what money

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1 author.

Mohammad FraiwanDepartment of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.ORCID 0000-0001-6352-5275

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sandflies, small insects primarily from the Psychodidae family, are commonly found in sandy, tropical, and subtropical regions. Most active during dawn and dusk, female sandflies feed on blood to facilitate egg production. In doing so, they can transmit infectious diseases that may cause symptoms such as fever, headaches, muscle pain, anemia, skin rashes, and ulcers. Importantly, sandflies are species-specific in their disease transmission. Determining the gender and species of sandflies typically involves examining their morphology and internal anatomy using established identification keys. However, this process requires expert knowledge and is labor-intensive, time-consuming, and prone to misidentification. In this paper, we develop a highly accurate and efficient convolutional network model that utilizes pharyngeal and genital images of sandfly samples to classify the sex and species of three sandfly species (i.e.,

Indexed as

convolutional neural networksPhlebotomus alexandriPhlebotomus papatasiPhlebotomus sergentisex identificationtaxonomy

Identifiers

PMID39765616

DOI10.3390/ani14243712

PMCPMC11672608

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.