Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination
Joint Authors
Xiao, Ting
Liu, Lei
Li, Kai
Qin, Wenjian
Li, Zhicheng
Yu, Shaode
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-21
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This research aims to address the problem of discriminating benign cysts from malignant masses in breast ultrasound (BUS) images based on Convolutional Neural Networks (CNNs).
The biopsy-proven benchmarking dataset was built from 1422 patient cases containing a total of 2058 breast ultrasound masses, comprising 1370 benign and 688 malignant lesions.
Three transferred models, InceptionV3, ResNet50, and Xception, a CNN model with three convolutional layers (CNN3), and traditional machine learning-based model with hand-crafted features were developed for differentiating benign and malignant tumors from BUS data.
Cross-validation results have demonstrated that the transfer learning method outperformed the traditional machine learning model and the CNN3 model, where the transferred InceptionV3 achieved the best performance with an accuracy of 85.13% and an AUC of 0.91.
Moreover, classification models based on deep features extracted from the transferred models were also built, where the model with combined features extracted from all three transferred models achieved the best performance with an accuracy of 89.44% and an AUC of 0.93 on an independent test set.
American Psychological Association (APA)
Xiao, Ting& Liu, Lei& Li, Kai& Qin, Wenjian& Yu, Shaode& Li, Zhicheng. 2018. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination. BioMed Research International،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1126783
Modern Language Association (MLA)
Xiao, Ting…[et al.]. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination. BioMed Research International No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1126783
American Medical Association (AMA)
Xiao, Ting& Liu, Lei& Li, Kai& Qin, Wenjian& Yu, Shaode& Li, Zhicheng. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1126783
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126783