The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries.

Fowler, Mark T; Lees, Rosemary S; Fagbohoun, Josias; Matowo, Nancy S; Ngufor, Corine; Protopopoff, Natacha; Spiers, Angus

Fowler, Mark T; Lees, Rosemary S; Fagbohoun, Josias; Matowo, Nancy S; Ngufor, Corine; Protopopoff, Natacha; Spiers, Angus.

Afiliación

Fowler MT; Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK.
Lees RS; Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK.
Fagbohoun J; Centre de Recherche Entomologique de Cotonou (CREC), Cotonou BP 2604, Benin.
Matowo NS; Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.
Ngufor C; Mwanza Medical Research Centre, Department of Parasitology, National Institute for Medical Research, Mwanza P.O. Box 1462, Tanzania.
Protopopoff N; Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.
Spiers A; Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.

Insects ; 12(12)2021 Dec 17.

Article en En | MEDLINE | ID: mdl-34940222

Palabras clave

Anopheles mosquito; automated identification; convolutional neural network; fertility; image classification; machine learning; ovary development; pyriproxyfen (PPF); side-effects

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Insects Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Suiza

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