Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination.
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...
| Publicado en: | BioMed Research International Vol. 2018; pp. 1 - 10 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/21/2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130304828&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130304828 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/21/2018 vid: 2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 130304828 130304828 130304828 10.1155/2018/4605191 130304828 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination. aug: au: Xiao, Ting Liu, Lei Li, Kai Qin, Wenjian Yu, Shaode Li, Zhicheng affil: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China sug: subj: Neural Networks (Computer) Cysts Ultrasonography Breast Neoplasms Ultrasonography Ultrasonography Methods Breast Neoplasms Pathology Human Biopsy Benchmarking Machine Learning Models, Theoretical Validity ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|