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convolutional neural networks

Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks

August 25, 2021 - 16:07 -- Open Access
Kittichai V, Kaewthamasorn M, Thanee S, Jomtarak R, Klanboot K, Naing KM, Tongloy T, Chuwongin S, Boonsang S
Sci Rep. 2021 Aug 19;11(1):16919

The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages.

Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks

December 23, 2020 - 09:33 -- Open Access
Couret J, Moreira DC, Bernier D, Loberti AM, Dotson EM, Alvarez M
PLoS Negl Trop Dis. 2020 Dec 17;14(12):e0008904

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiae complex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes.

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