Efficient and accurate identification of ear diseases using an ensemble deep learning model

Abstract Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xinyu Zeng, Zifan Jiang, Wen Luo, Honggui Li, Hongye Li, Guo Li, Jingyong Shi, Kangjie Wu, Tong Liu, Xing Lin, Fusen Wang, Zhenzhang Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/68a9b10173d3402c9eaba16cfefee045
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:68a9b10173d3402c9eaba16cfefee045
record_format	dspace
spelling	oai:doaj.org-article:68a9b10173d3402c9eaba16cfefee0452021-12-02T16:53:12ZEfficient and accurate identification of ear diseases using an ensemble deep learning model10.1038/s41598-021-90345-w2045-2322https://doaj.org/article/68a9b10173d3402c9eaba16cfefee0452021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90345-whttps://doaj.org/toc/2045-2322Abstract Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in clinical cases to achieve an automatic diagnosis of ear diseases in real time. A total of 20,542 endoscopic images were employed to train nine common deep convolution neural networks. According to the characteristics of the eardrum and external auditory canal, eight kinds of ear diseases were classified, involving the majority of ear diseases, such as normal, Cholestestoma of the middle ear, Chronic suppurative otitis media, External auditory cana bleeding, Impacted cerumen, Otomycosis external, Secretory otitis media, Tympanic membrane calcification. After we evaluate these optimization schemes, two best performance models are selected to combine the ensemble classifiers with real-time automatic classification. Based on accuracy and training time, we choose a transferring learning model based on DensNet-BC169 and DensNet-BC1615, getting a result that each model has obvious improvement by using these two ensemble classifiers, and has an average accuracy of 95.59%. Considering the dependence of classifier performance on data size in transfer learning, we evaluate the high accuracy of the current model that can be attributed to large databases. Current studies are unparalleled regarding disease diversity and diagnostic precision. The real-time classifier trains the data under different acquisition conditions, which is suitable for real cases. According to this study, in the clinical case, the deep learning model is of great use in the early detection and remedy of ear diseases.Xinyu ZengZifan JiangWen LuoHonggui LiHongye LiGuo LiJingyong ShiKangjie WuTong LiuXing LinFusen WangZhenzhang LiNature 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 Xinyu Zeng Zifan Jiang Wen Luo Honggui Li Hongye Li Guo Li Jingyong Shi Kangjie Wu Tong Liu Xing Lin Fusen Wang Zhenzhang Li Efficient and accurate identification of ear diseases using an ensemble deep learning model
description	Abstract Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in clinical cases to achieve an automatic diagnosis of ear diseases in real time. A total of 20,542 endoscopic images were employed to train nine common deep convolution neural networks. According to the characteristics of the eardrum and external auditory canal, eight kinds of ear diseases were classified, involving the majority of ear diseases, such as normal, Cholestestoma of the middle ear, Chronic suppurative otitis media, External auditory cana bleeding, Impacted cerumen, Otomycosis external, Secretory otitis media, Tympanic membrane calcification. After we evaluate these optimization schemes, two best performance models are selected to combine the ensemble classifiers with real-time automatic classification. Based on accuracy and training time, we choose a transferring learning model based on DensNet-BC169 and DensNet-BC1615, getting a result that each model has obvious improvement by using these two ensemble classifiers, and has an average accuracy of 95.59%. Considering the dependence of classifier performance on data size in transfer learning, we evaluate the high accuracy of the current model that can be attributed to large databases. Current studies are unparalleled regarding disease diversity and diagnostic precision. The real-time classifier trains the data under different acquisition conditions, which is suitable for real cases. According to this study, in the clinical case, the deep learning model is of great use in the early detection and remedy of ear diseases.
format	article
author	Xinyu Zeng Zifan Jiang Wen Luo Honggui Li Hongye Li Guo Li Jingyong Shi Kangjie Wu Tong Liu Xing Lin Fusen Wang Zhenzhang Li
author_facet	Xinyu Zeng Zifan Jiang Wen Luo Honggui Li Hongye Li Guo Li Jingyong Shi Kangjie Wu Tong Liu Xing Lin Fusen Wang Zhenzhang Li
author_sort	Xinyu Zeng
title	Efficient and accurate identification of ear diseases using an ensemble deep learning model
title_short	Efficient and accurate identification of ear diseases using an ensemble deep learning model
title_full	Efficient and accurate identification of ear diseases using an ensemble deep learning model
title_fullStr	Efficient and accurate identification of ear diseases using an ensemble deep learning model
title_full_unstemmed	Efficient and accurate identification of ear diseases using an ensemble deep learning model
title_sort	efficient and accurate identification of ear diseases using an ensemble deep learning model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/68a9b10173d3402c9eaba16cfefee045
work_keys_str_mv	AT xinyuzeng efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT zifanjiang efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT wenluo efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT hongguili efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT hongyeli efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT guoli efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT jingyongshi efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT kangjiewu efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT tongliu efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT xinglin efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT fusenwang efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel AT zhenzhangli efficientandaccurateidentificationofeardiseasesusinganensembledeeplearningmodel
_version_	1718382864313090048

Efficient and accurate identification of ear diseases using an ensemble deep learning model

Ejemplares similares