TY - JOUR
T1 - Classification of breast mass in 3D ultrasound images with annotations based on convolutional neural networks
AU - Kong, Xiaohan
AU - Tan, Tao
AU - Bao, Lingyun
AU - Wang, Guangzhi
N1 - Publisher Copyright:
© 2018 Chinese Academy of Medical Sciences. All rights reserved.
PY - 2018/8/20
Y1 - 2018/8/20
N2 - The automatic classification of breast tumor in ultrasound images is of great significance to improve doctors' efficiency and reduce the rate of misdiagnosis. The novel 3D breast ultrasound data contains more information for diagnosis, but images from different directions have their distinct performance as a result of this ultrasound imaging mechanism. For this breast ultrasound data, this paper designed three kinds of convolutional neural network model using its flexibility and characteristic of learning automatically, and the three models were able to accept transverse plane images, transverse plane and coronal plane images, images and annotations information. The effects of different information fusion on the accuracy of breast tumor classification were investigated. A dataset contains 880 images (i. e., 401 benign images, 479 malign images) and their annotations were employed, and we performed 5-fold cross validation to calculate the accuracy and AUC of each model. The experimental results indicated that the models designed in this paper can deal with the images and annotations simultaneously. Compared with the single-input model, the multi-information fusion model improved the accuracy of classification by 2.91%, and achieved the accuracy of 75.11% and AUC of 0.8294. The proposed models provided a reference for the classification application of convolutional neural networks with multi-information fusion.
AB - The automatic classification of breast tumor in ultrasound images is of great significance to improve doctors' efficiency and reduce the rate of misdiagnosis. The novel 3D breast ultrasound data contains more information for diagnosis, but images from different directions have their distinct performance as a result of this ultrasound imaging mechanism. For this breast ultrasound data, this paper designed three kinds of convolutional neural network model using its flexibility and characteristic of learning automatically, and the three models were able to accept transverse plane images, transverse plane and coronal plane images, images and annotations information. The effects of different information fusion on the accuracy of breast tumor classification were investigated. A dataset contains 880 images (i. e., 401 benign images, 479 malign images) and their annotations were employed, and we performed 5-fold cross validation to calculate the accuracy and AUC of each model. The experimental results indicated that the models designed in this paper can deal with the images and annotations simultaneously. Compared with the single-input model, the multi-information fusion model improved the accuracy of classification by 2.91%, and achieved the accuracy of 75.11% and AUC of 0.8294. The proposed models provided a reference for the classification application of convolutional neural networks with multi-information fusion.
KW - 3D breast ultrasound
KW - Convolutional neural networks
KW - Medical image classification
KW - Multi-information fusion
UR - http://www.scopus.com/inward/record.url?scp=85054786883&partnerID=8YFLogxK
U2 - 10.3969/j.issn.0258-8021.2018.04.004
DO - 10.3969/j.issn.0258-8021.2018.04.004
M3 - Article
AN - SCOPUS:85054786883
SN - 0258-8021
VL - 37
SP - 414
EP - 422
JO - Chinese Journal of Biomedical Engineering
JF - Chinese Journal of Biomedical Engineering
IS - 4
ER -