Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

Abstract Background The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to imp...

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Autores principales:	He Ma, Ronghui Tian, Hong Li, Hang Sun, Guoxiu Lu, Ruibo Liu, Zhiguo Wang
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Convolutional Neural Network Deep learning Data augmentation Breast ultrasound tumor images Classification Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/3547486f0dda4b46a3ec8400cf4e253f
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spelling	oai:doaj.org-article:3547486f0dda4b46a3ec8400cf4e253f2021-11-21T12:12:15ZFus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images10.1186/s12938-021-00950-z1475-925Xhttps://doaj.org/article/3547486f0dda4b46a3ec8400cf4e253f2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12938-021-00950-zhttps://doaj.org/toc/1475-925XAbstract Background The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible. Results The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. Conclusions The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. Methods The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.He MaRonghui TianHong LiHang SunGuoxiu LuRuibo LiuZhiguo WangBMCarticleConvolutional Neural NetworkDeep learningData augmentationBreast ultrasound tumor imagesClassificationMedical technologyR855-855.5ENBioMedical Engineering OnLine, Vol 20, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Convolutional Neural Network Deep learning Data augmentation Breast ultrasound tumor images Classification Medical technology R855-855.5
spellingShingle	Convolutional Neural Network Deep learning Data augmentation Breast ultrasound tumor images Classification Medical technology R855-855.5 He Ma Ronghui Tian Hong Li Hang Sun Guoxiu Lu Ruibo Liu Zhiguo Wang Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images
description	Abstract Background The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible. Results The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. Conclusions The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. Methods The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.
format	article
author	He Ma Ronghui Tian Hong Li Hang Sun Guoxiu Lu Ruibo Liu Zhiguo Wang
author_facet	He Ma Ronghui Tian Hong Li Hang Sun Guoxiu Lu Ruibo Liu Zhiguo Wang
author_sort	He Ma
title	Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images
title_short	Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images
title_full	Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images
title_fullStr	Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images
title_full_unstemmed	Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images
title_sort	fus2net: a novel convolutional neural network for classification of benign and malignant breast tumor in ultrasound images
publisher	BMC
publishDate	2021
url	https://doaj.org/article/3547486f0dda4b46a3ec8400cf4e253f
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Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

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