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

Abstract 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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Veerayuth Kittichai, Morakot Kaewthamasorn, Suchansa Thanee, Rangsan Jomtarak, Kamonpob Klanboot, Kaung Myat Naing, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3b56ebdf33274ba59a8fde89d71210c6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3b56ebdf33274ba59a8fde89d71210c6
record_format	dspace
spelling	oai:doaj.org-article:3b56ebdf33274ba59a8fde89d71210c62021-12-02T18:51:41ZClassification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks10.1038/s41598-021-96475-52045-2322https://doaj.org/article/3b56ebdf33274ba59a8fde89d71210c62021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96475-5https://doaj.org/toc/2045-2322Abstract 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. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.Veerayuth KittichaiMorakot KaewthamasornSuchansa ThaneeRangsan JomtarakKamonpob KlanbootKaung Myat NaingTeerawat TongloySanthad ChuwonginSiridech BoonsangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Veerayuth Kittichai Morakot Kaewthamasorn Suchansa Thanee Rangsan Jomtarak Kamonpob Klanboot Kaung Myat Naing Teerawat Tongloy Santhad Chuwongin Siridech Boonsang Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
description	Abstract 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. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.
format	article
author	Veerayuth Kittichai Morakot Kaewthamasorn Suchansa Thanee Rangsan Jomtarak Kamonpob Klanboot Kaung Myat Naing Teerawat Tongloy Santhad Chuwongin Siridech Boonsang
author_facet	Veerayuth Kittichai Morakot Kaewthamasorn Suchansa Thanee Rangsan Jomtarak Kamonpob Klanboot Kaung Myat Naing Teerawat Tongloy Santhad Chuwongin Siridech Boonsang
author_sort	Veerayuth Kittichai
title	Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
title_short	Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
title_full	Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
title_fullStr	Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
title_full_unstemmed	Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
title_sort	classification for avian malaria parasite plasmodium gallinaceum blood stages by using deep convolutional neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3b56ebdf33274ba59a8fde89d71210c6
work_keys_str_mv	AT veerayuthkittichai classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks AT morakotkaewthamasorn classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks AT suchansathanee classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks AT rangsanjomtarak classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks AT kamonpobklanboot classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks AT kaungmyatnaing classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks AT teerawattongloy classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks AT santhadchuwongin classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks AT siridechboonsang classificationforavianmalariaparasiteplasmodiumgallinaceumbloodstagesbyusingdeepconvolutionalneuralnetworks
_version_	1718377378744369152

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

Ejemplares similares