Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks

Abstract The use of convolutional neural networks (CNNs) has dramatically advanced our ability to recognize images with machine learning methods. We aimed to construct a CNN that could recognize the anatomical location of esophagogastroduodenoscopy (EGD) images in an appropriate manner. A CNN-based...

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Autores principales:	Hirotoshi Takiyama, Tsuyoshi Ozawa, Soichiro Ishihara, Mitsuhiro Fujishiro, Satoki Shichijo, Shuhei Nomura, Motoi Miura, Tomohiro Tada
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/73bf6b140e564453a3cc9dc41b8d0606
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spelling	oai:doaj.org-article:73bf6b140e564453a3cc9dc41b8d06062021-12-02T12:32:35ZAutomatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks10.1038/s41598-018-25842-62045-2322https://doaj.org/article/73bf6b140e564453a3cc9dc41b8d06062018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25842-6https://doaj.org/toc/2045-2322Abstract The use of convolutional neural networks (CNNs) has dramatically advanced our ability to recognize images with machine learning methods. We aimed to construct a CNN that could recognize the anatomical location of esophagogastroduodenoscopy (EGD) images in an appropriate manner. A CNN-based diagnostic program was constructed based on GoogLeNet architecture, and was trained with 27,335 EGD images that were categorized into four major anatomical locations (larynx, esophagus, stomach and duodenum) and three subsequent sub-classifications for stomach images (upper, middle, and lower regions). The performance of the CNN was evaluated in an independent validation set of 17,081 EGD images by drawing receiver operating characteristics (ROC) curves and calculating the area under the curves (AUCs). ROC curves showed high performance of the trained CNN to classify the anatomical location of EGD images with AUCs of 1.00 for larynx and esophagus images, and 0.99 for stomach and duodenum images. Furthermore, the trained CNN could recognize specific anatomical locations within the stomach, with AUCs of 0.99 for the upper, middle, and lower stomach. In conclusion, the trained CNN showed robust performance in its ability to recognize the anatomical location of EGD images, highlighting its significant potential for future application as a computer-aided EGD diagnostic system.Hirotoshi TakiyamaTsuyoshi OzawaSoichiro IshiharaMitsuhiro FujishiroSatoki ShichijoShuhei NomuraMotoi MiuraTomohiro TadaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-8 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hirotoshi Takiyama Tsuyoshi Ozawa Soichiro Ishihara Mitsuhiro Fujishiro Satoki Shichijo Shuhei Nomura Motoi Miura Tomohiro Tada Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
description	Abstract The use of convolutional neural networks (CNNs) has dramatically advanced our ability to recognize images with machine learning methods. We aimed to construct a CNN that could recognize the anatomical location of esophagogastroduodenoscopy (EGD) images in an appropriate manner. A CNN-based diagnostic program was constructed based on GoogLeNet architecture, and was trained with 27,335 EGD images that were categorized into four major anatomical locations (larynx, esophagus, stomach and duodenum) and three subsequent sub-classifications for stomach images (upper, middle, and lower regions). The performance of the CNN was evaluated in an independent validation set of 17,081 EGD images by drawing receiver operating characteristics (ROC) curves and calculating the area under the curves (AUCs). ROC curves showed high performance of the trained CNN to classify the anatomical location of EGD images with AUCs of 1.00 for larynx and esophagus images, and 0.99 for stomach and duodenum images. Furthermore, the trained CNN could recognize specific anatomical locations within the stomach, with AUCs of 0.99 for the upper, middle, and lower stomach. In conclusion, the trained CNN showed robust performance in its ability to recognize the anatomical location of EGD images, highlighting its significant potential for future application as a computer-aided EGD diagnostic system.
format	article
author	Hirotoshi Takiyama Tsuyoshi Ozawa Soichiro Ishihara Mitsuhiro Fujishiro Satoki Shichijo Shuhei Nomura Motoi Miura Tomohiro Tada
author_facet	Hirotoshi Takiyama Tsuyoshi Ozawa Soichiro Ishihara Mitsuhiro Fujishiro Satoki Shichijo Shuhei Nomura Motoi Miura Tomohiro Tada
author_sort	Hirotoshi Takiyama
title	Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
title_short	Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
title_full	Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
title_fullStr	Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
title_full_unstemmed	Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
title_sort	automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/73bf6b140e564453a3cc9dc41b8d0606
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_version_	1718394040932630528

Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks

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