Prediction of locations in medical images using orthogonal neural networks

Background/Purpose: An orthogonal neural network (ONN), a new deep-learning structure for medical image localization, is developed and presented in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN). Materials and methods: The diagnostic pe...

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Autores principales:	Jong Soo Kim, Yongil Cho, Tae Ho Lim
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Deep learning Glottis Localization Orthogonal neural network Pneumothorax Medical physics. Medical radiology. Nuclear medicine R895-920
Acceso en línea:	https://doaj.org/article/af5f88bacf6942aaa8b69e1c90fccb40
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spelling	oai:doaj.org-article:af5f88bacf6942aaa8b69e1c90fccb402021-12-02T05:01:48ZPrediction of locations in medical images using orthogonal neural networks2352-047710.1016/j.ejro.2021.100388https://doaj.org/article/af5f88bacf6942aaa8b69e1c90fccb402021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S235204772100068Xhttps://doaj.org/toc/2352-0477Background/Purpose: An orthogonal neural network (ONN), a new deep-learning structure for medical image localization, is developed and presented in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN). Materials and methods: The diagnostic performance of ONN for detecting the location of pneumothorax in chest X-rays was assessed and compared to that of CNN. In addition, ONN and CNN were applied to predict the location of the glottis in laryngeal images. Results: An area under the receiver operating characteristic (ROC) curve (AUC) of 0.870, an accuracy of 85.3%, a sensitivity of 75.0%, and a specificity of 86.5% were achieved by applying ONN to detect the location of pneumothorax in chest X-rays; the ONN outperformed the CNN. By applying ONN to predict the location of the glottis in laryngeal images, we achieved the accurate prediction rate of 70.5% and the adjacent prediction rate of 20.5%. Conclusions: This study demonstrated that an ONN can be used as a quick selection criterion to compare fully-connected small artificial neural network (ANN) models for image localization. The time it took to train an ONN was about 10% of the time using a CNN on images of a given input resolution. Our approach could accurately predict locations in medical images, reduce the time delay in diagnosing urgent diseases, and increase the effectiveness of clinical practice and patient care.Jong Soo KimYongil ChoTae Ho LimElsevierarticleDeep learningGlottisLocalizationOrthogonal neural networkPneumothoraxMedical physics. Medical radiology. Nuclear medicineR895-920ENEuropean Journal of Radiology Open, Vol 8, Iss , Pp 100388- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning Glottis Localization Orthogonal neural network Pneumothorax Medical physics. Medical radiology. Nuclear medicine R895-920
spellingShingle	Deep learning Glottis Localization Orthogonal neural network Pneumothorax Medical physics. Medical radiology. Nuclear medicine R895-920 Jong Soo Kim Yongil Cho Tae Ho Lim Prediction of locations in medical images using orthogonal neural networks
description	Background/Purpose: An orthogonal neural network (ONN), a new deep-learning structure for medical image localization, is developed and presented in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN). Materials and methods: The diagnostic performance of ONN for detecting the location of pneumothorax in chest X-rays was assessed and compared to that of CNN. In addition, ONN and CNN were applied to predict the location of the glottis in laryngeal images. Results: An area under the receiver operating characteristic (ROC) curve (AUC) of 0.870, an accuracy of 85.3%, a sensitivity of 75.0%, and a specificity of 86.5% were achieved by applying ONN to detect the location of pneumothorax in chest X-rays; the ONN outperformed the CNN. By applying ONN to predict the location of the glottis in laryngeal images, we achieved the accurate prediction rate of 70.5% and the adjacent prediction rate of 20.5%. Conclusions: This study demonstrated that an ONN can be used as a quick selection criterion to compare fully-connected small artificial neural network (ANN) models for image localization. The time it took to train an ONN was about 10% of the time using a CNN on images of a given input resolution. Our approach could accurately predict locations in medical images, reduce the time delay in diagnosing urgent diseases, and increase the effectiveness of clinical practice and patient care.
format	article
author	Jong Soo Kim Yongil Cho Tae Ho Lim
author_facet	Jong Soo Kim Yongil Cho Tae Ho Lim
author_sort	Jong Soo Kim
title	Prediction of locations in medical images using orthogonal neural networks
title_short	Prediction of locations in medical images using orthogonal neural networks
title_full	Prediction of locations in medical images using orthogonal neural networks
title_fullStr	Prediction of locations in medical images using orthogonal neural networks
title_full_unstemmed	Prediction of locations in medical images using orthogonal neural networks
title_sort	prediction of locations in medical images using orthogonal neural networks
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/af5f88bacf6942aaa8b69e1c90fccb40
work_keys_str_mv	AT jongsookim predictionoflocationsinmedicalimagesusingorthogonalneuralnetworks AT yongilcho predictionoflocationsinmedicalimagesusingorthogonalneuralnetworks AT taeholim predictionoflocationsinmedicalimagesusingorthogonalneuralnetworks
_version_	1718400786840420352

Prediction of locations in medical images using orthogonal neural networks

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