Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps

Ultrasound (US) imaging is widely utilized as a diagnostic screening method, and deep learning has recently drawn attention for the analysis of US images for the pathological status of tissues. While low image quality and poor reproducibility are the common obstacles in US analysis, the small size o...

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Autores principales:	Hyungsuk Kim, Juyoung Park, Hakjoon Lee, Geuntae Im, Jongsoo Lee, Ki-Baek Lee, Heung Jae Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	medical ultrasound breast US images deep learning convolutional neural network B-mode image entropy image Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/890fb61d22b74595ab69b870c3c0e465
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id	oai:doaj.org-article:890fb61d22b74595ab69b870c3c0e465
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spelling	oai:doaj.org-article:890fb61d22b74595ab69b870c3c0e4652021-11-11T15:16:03ZClassification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps10.3390/app1121102162076-3417https://doaj.org/article/890fb61d22b74595ab69b870c3c0e4652021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10216https://doaj.org/toc/2076-3417Ultrasound (US) imaging is widely utilized as a diagnostic screening method, and deep learning has recently drawn attention for the analysis of US images for the pathological status of tissues. While low image quality and poor reproducibility are the common obstacles in US analysis, the small size of the dataset is a new limitation for deep learning due to lack of generalization. In this work, a convolutional neural network (CNN) using multiple feature maps, such as entropy and phase images, as well as a B-mode image, was proposed to classify breast US images. Although B-mode images contain both anatomical and textual information, traditional CNNs experience difficulties in abstracting features automatically, especially with small datasets. For the proposed CNN framework, two distinct feature maps were obtained from a B-mode image and utilized as new inputs for training the CNN. These feature maps can also be made from the evaluation data and applied to the CNN separately for the final classification decision. The experimental results with 780 breast US images in three categories of benign, malignant, and normal, showed that the proposed CNN framework using multiple feature maps exhibited better performances than the traditional CNN with B-mode only for most deep network models.Hyungsuk KimJuyoung ParkHakjoon LeeGeuntae ImJongsoo LeeKi-Baek LeeHeung Jae LeeMDPI AGarticlemedical ultrasoundbreast US imagesdeep learningconvolutional neural networkB-mode imageentropy imageTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10216, p 10216 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	medical ultrasound breast US images deep learning convolutional neural network B-mode image entropy image Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	medical ultrasound breast US images deep learning convolutional neural network B-mode image entropy image Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Hyungsuk Kim Juyoung Park Hakjoon Lee Geuntae Im Jongsoo Lee Ki-Baek Lee Heung Jae Lee Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps
description	Ultrasound (US) imaging is widely utilized as a diagnostic screening method, and deep learning has recently drawn attention for the analysis of US images for the pathological status of tissues. While low image quality and poor reproducibility are the common obstacles in US analysis, the small size of the dataset is a new limitation for deep learning due to lack of generalization. In this work, a convolutional neural network (CNN) using multiple feature maps, such as entropy and phase images, as well as a B-mode image, was proposed to classify breast US images. Although B-mode images contain both anatomical and textual information, traditional CNNs experience difficulties in abstracting features automatically, especially with small datasets. For the proposed CNN framework, two distinct feature maps were obtained from a B-mode image and utilized as new inputs for training the CNN. These feature maps can also be made from the evaluation data and applied to the CNN separately for the final classification decision. The experimental results with 780 breast US images in three categories of benign, malignant, and normal, showed that the proposed CNN framework using multiple feature maps exhibited better performances than the traditional CNN with B-mode only for most deep network models.
format	article
author	Hyungsuk Kim Juyoung Park Hakjoon Lee Geuntae Im Jongsoo Lee Ki-Baek Lee Heung Jae Lee
author_facet	Hyungsuk Kim Juyoung Park Hakjoon Lee Geuntae Im Jongsoo Lee Ki-Baek Lee Heung Jae Lee
author_sort	Hyungsuk Kim
title	Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps
title_short	Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps
title_full	Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps
title_fullStr	Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps
title_full_unstemmed	Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps
title_sort	classification for breast ultrasound using convolutional neural network with multiple time-domain feature maps
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/890fb61d22b74595ab69b870c3c0e465
work_keys_str_mv	AT hyungsukkim classificationforbreastultrasoundusingconvolutionalneuralnetworkwithmultipletimedomainfeaturemaps AT juyoungpark classificationforbreastultrasoundusingconvolutionalneuralnetworkwithmultipletimedomainfeaturemaps AT hakjoonlee classificationforbreastultrasoundusingconvolutionalneuralnetworkwithmultipletimedomainfeaturemaps AT geuntaeim classificationforbreastultrasoundusingconvolutionalneuralnetworkwithmultipletimedomainfeaturemaps AT jongsoolee classificationforbreastultrasoundusingconvolutionalneuralnetworkwithmultipletimedomainfeaturemaps AT kibaeklee classificationforbreastultrasoundusingconvolutionalneuralnetworkwithmultipletimedomainfeaturemaps AT heungjaelee classificationforbreastultrasoundusingconvolutionalneuralnetworkwithmultipletimedomainfeaturemaps
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Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps

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