期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 57, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2019.101789
关键词
Breast cancer histopathology images; Uninvolved images; Convolutional neural networks; Double deep transfer learning; Interactive cross-task extreme learning machine
资金
- National Natural Science Foundation of China NSFC [61771080, 61571069]
- Fundamental and Advanced Research Project of Chongqing [cstc2018jcyjAX0779]
- Fundamental Research Funds for the Central Universities [2019CDQYTX019, 2019CDCGTX306]
- Open Project Program of the National Laboratory of Pattern Recognition (NLPR) [201800011]
Automatic classification of breast histopathology images plays a key role in computer-aided breast cancer diagnosis. However, feature-based classification methods rely on the accurate cell segmentation and feature extraction. Due to overlapping cells, dust, impurities and uneven irradiation the accurate segmentation and efficient feature extraction are still challenging. In order to overcome the above difficulties and limited breast histopathology images, in this paper, a hybrid structure which includes a double deep transfer learning ((DTL)-T-2) and interactive cross-task extreme learning machine (ICELM) is proposed based on feature extraction and representation ability of CNN and classification robustness of ELM. First, high level features are extracted using deep transfer learning and double-step deep transfer learning. Then, the high level feature sets are jointly used as regularization terms to further improve classification performance in interactive cross task extreme learning machine. The proposed method was tested on 134 breast cancer histopathology images. Results show that our method has achieved remarkable performance in classification accuracy (96.67%, 96.96%, 98.18%). From the experiment result, the proposed method is promising for providing an efficient tool for breast cancer classification in clinical settings. (C) 2019 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据