Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix

Yan Hao; Li Zhang; Shichang Qiao; Yanping Bai; Rong Cheng; Hongxin Xue; Yuchao Hou; Wendong Zhang; Guojun Zhang

doi:10.1371/journal.pone.0267955

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix

PLoS One. 2022 May 5;17(5):e0267955. doi: 10.1371/journal.pone.0267955. eCollection 2022.

Authors

Yan Hao^{1

2}, Li Zhang³, Shichang Qiao³, Yanping Bai³, Rong Cheng³, Hongxin Xue⁴, Yuchao Hou², Wendong Zhang⁵, Guojun Zhang⁵

Affiliations

¹ Department of Mathematics, Taiyuan Normal University, Taiyuan, China.
² School of Information and Communication Engineering, North University of China, Taiyuan, China.
³ Department of Mathematics, School of Science, North University of China, Taiyuan, China.
⁴ Data Science and Technology, North University of China, Taiyuan, China.
⁵ School of Instrument and Electronics, Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China.

Abstract

Breast cancer is regarded as the leading killer of women today. The early diagnosis and treatment of breast cancer is the key to improving the survival rate of patients. A method of breast cancer histopathological images recognition based on deep semantic features and gray level co-occurrence matrix (GLCM) features is proposed in this paper. Taking the pre-trained DenseNet201 as the basic model, part of the convolutional layer features of the last dense block are extracted as the deep semantic features, which are then fused with the three-channel GLCM features, and the support vector machine (SVM) is used for classification. For the BreaKHis dataset, we explore the classification problems of magnification specific binary (MSB) classification and magnification independent binary (MIB) classification, and compared the performance with the seven baseline models of AlexNet, VGG16, ResNet50, GoogLeNet, DenseNet201, SqueezeNet and Inception-ResNet-V2. The experimental results show that the method proposed in this paper performs better than the pre-trained baseline models in MSB and MIB classification problems. The highest image-level recognition accuracy of 40×, 100×, 200×, 400× is 96.75%, 95.21%, 96.57%, and 93.15%, respectively. And the highest patient-level recognition accuracy of the four magnifications is 96.33%, 95.26%, 96.09%, and 92.99%, respectively. The image-level and patient-level recognition accuracy for MIB classification is 95.56% and 95.54%, respectively. In addition, the recognition accuracy of the method in this paper is comparable to some state-of-the-art methods.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Breast / pathology
Breast Neoplasms* / pathology
Female
Humans
Neural Networks, Computer*
Semantics
Support Vector Machine

Grants and funding

This research was funded by the National Natural Science Foundation of China as National Major Scientific Instruments Development Project (Grant No. 61927807), the National Natural Science Foundation of China (Grant No. 51875535, 61774137), the Key Research and Development Projects of Shanxi Province (Grant No. 201903D121156), the Fundamental Research Program of Shanxi Province (Grant No. 202103021223189, 202103021224195), the National Key Research and Development Project (Grant No. 2019YFC0119800) and the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (Grant No.18005520). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.