Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.

Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to evaluate a complete sample; additionally, it poss...

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Autores principales:	Taehan Koo, Moon Hwan Kim, Mihn-Sook Jue
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/213bf34204a445c4989d5d58abf5d24f
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spelling	oai:doaj.org-article:213bf34204a445c4989d5d58abf5d24f2021-12-02T20:17:54ZAutomated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.1932-620310.1371/journal.pone.0256290https://doaj.org/article/213bf34204a445c4989d5d58abf5d24f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256290https://doaj.org/toc/1932-6203Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to evaluate a complete sample; additionally, it possesses the disadvantage of inconsistent reliability as the accuracy of the reading may differ depending on the performer's skill. This study aims at detecting hyphae more quickly, conveniently, and consistently through deep learning using images obtained from microscopy used in real-world practice. An object detection convolutional neural network, YOLO v4, was trained on microscopy images with magnifications of 100×, 40×, and (100+40)×. The study was conducted at the Department of Dermatology at Veterans Health Service Medical Center, Seoul, Korea between January 1, 2019 and December 31, 2019, using 3,707 images (1,255 images for training, 1,645 images for testing). The average precision was used to evaluate the accuracy of object detection. Precision recall curve analysis was performed for the hyphal location determination, and receiver operating characteristic curve analysis was performed on the image classification. The F1 score, sensitivity, and specificity values were used as measures of the overall performance. The sensitivity and specificity were, respectively, 95.2% and 100% in the 100× data model, and 99% and 86.6% in the 40× data model; the sensitivity and specificity in the combined (100+40)× data model were 93.2% and 89%, respectively. The performance of our model had high sensitivity and specificity, indicating that hyphae can be detected with reliable accuracy. Thus, our deep learning-based autodetection model can detect hyphae in microscopic images obtained from real-world practice. We aim to develop an automatic hyphae detection system that can be utilized in real-world practice through continuous research.Taehan KooMoon Hwan KimMihn-Sook JuePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256290 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Taehan Koo Moon Hwan Kim Mihn-Sook Jue Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.
description	Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to evaluate a complete sample; additionally, it possesses the disadvantage of inconsistent reliability as the accuracy of the reading may differ depending on the performer's skill. This study aims at detecting hyphae more quickly, conveniently, and consistently through deep learning using images obtained from microscopy used in real-world practice. An object detection convolutional neural network, YOLO v4, was trained on microscopy images with magnifications of 100×, 40×, and (100+40)×. The study was conducted at the Department of Dermatology at Veterans Health Service Medical Center, Seoul, Korea between January 1, 2019 and December 31, 2019, using 3,707 images (1,255 images for training, 1,645 images for testing). The average precision was used to evaluate the accuracy of object detection. Precision recall curve analysis was performed for the hyphal location determination, and receiver operating characteristic curve analysis was performed on the image classification. The F1 score, sensitivity, and specificity values were used as measures of the overall performance. The sensitivity and specificity were, respectively, 95.2% and 100% in the 100× data model, and 99% and 86.6% in the 40× data model; the sensitivity and specificity in the combined (100+40)× data model were 93.2% and 89%, respectively. The performance of our model had high sensitivity and specificity, indicating that hyphae can be detected with reliable accuracy. Thus, our deep learning-based autodetection model can detect hyphae in microscopic images obtained from real-world practice. We aim to develop an automatic hyphae detection system that can be utilized in real-world practice through continuous research.
format	article
author	Taehan Koo Moon Hwan Kim Mihn-Sook Jue
author_facet	Taehan Koo Moon Hwan Kim Mihn-Sook Jue
author_sort	Taehan Koo
title	Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.
title_short	Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.
title_full	Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.
title_fullStr	Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.
title_full_unstemmed	Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.
title_sort	automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/213bf34204a445c4989d5d58abf5d24f
work_keys_str_mv	AT taehankoo automateddetectionofsuperficialfungalinfectionsfrommicroscopicimagesthrougharegionalconvolutionalneuralnetwork AT moonhwankim automateddetectionofsuperficialfungalinfectionsfrommicroscopicimagesthrougharegionalconvolutionalneuralnetwork AT mihnsookjue automateddetectionofsuperficialfungalinfectionsfrommicroscopicimagesthrougharegionalconvolutionalneuralnetwork
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Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.

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