Cross-task extreme learning machine for breast cancer image classification with deep convolutional features

• DOI: 10.1016/j.bspc.2019.101789
• Publication Date: 2019-12-03T04:15:31Z
• Authors: Pin Wang; Qi Song; Yongming Li; Shanshan Lv; Jiaxin Wang; Linyu Li; HeHua Zhang
• Keywords: Extreme Learning Machine; Robustness; Transfer of learning; Feature Learning; Contextual image classification; Feature vector

Abstract 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 (D2TL) 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.