Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination
文献类型：期刊论文
| 作者 | Li, Kai; Yu, Shaode; Li, Zhicheng; Xiao, Ting; Liu, Lei; Qin, Wenjian |
| 刊名 | BIOMED RESEARCH INTERNATIONAL
 |
| 出版日期 | 2018 |
| 文献子类 | 期刊论文 |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14249]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Li, Kai,Yu, Shaode,Li, Zhicheng,et al. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination[J]. BIOMED RESEARCH INTERNATIONAL,2018. |
| APA | Li, Kai,Yu, Shaode,Li, Zhicheng,Xiao, Ting,Liu, Lei,&Qin, Wenjian.(2018).Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination.BIOMED RESEARCH INTERNATIONAL. |
| MLA | Li, Kai,et al."Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination".BIOMED RESEARCH INTERNATIONAL (2018). |
入库方式： OAI收割
来源：深圳先进技术研究院
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