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

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...

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Autores principales: Hao, Yan, Zhang, Li, Qiao, Shichang, Bai, Yanping, Cheng, Rong, Xue, Hongxin, Hou, Yuchao, Zhang, Wendong, Zhang, Guojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070886/
https://www.ncbi.nlm.nih.gov/pubmed/35511877
http://dx.doi.org/10.1371/journal.pone.0267955
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author Hao, Yan
Zhang, Li
Qiao, Shichang
Bai, Yanping
Cheng, Rong
Xue, Hongxin
Hou, Yuchao
Zhang, Wendong
Zhang, Guojun
author_facet Hao, Yan
Zhang, Li
Qiao, Shichang
Bai, Yanping
Cheng, Rong
Xue, Hongxin
Hou, Yuchao
Zhang, Wendong
Zhang, Guojun
author_sort Hao, Yan
collection PubMed
description 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.
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spelling pubmed-90708862022-05-06 Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix Hao, Yan Zhang, Li Qiao, Shichang Bai, Yanping Cheng, Rong Xue, Hongxin Hou, Yuchao Zhang, Wendong Zhang, Guojun PLoS One Research Article 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. Public Library of Science 2022-05-05 /pmc/articles/PMC9070886/ /pubmed/35511877 http://dx.doi.org/10.1371/journal.pone.0267955 Text en © 2022 Hao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hao, Yan
Zhang, Li
Qiao, Shichang
Bai, Yanping
Cheng, Rong
Xue, Hongxin
Hou, Yuchao
Zhang, Wendong
Zhang, Guojun
Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix
title Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix
title_full Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix
title_fullStr Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix
title_full_unstemmed Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix
title_short Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix
title_sort breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070886/
https://www.ncbi.nlm.nih.gov/pubmed/35511877
http://dx.doi.org/10.1371/journal.pone.0267955
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