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Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features
Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer his...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236881/ https://www.ncbi.nlm.nih.gov/pubmed/34195073 http://dx.doi.org/10.3389/fonc.2021.657560 |
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author | Hao, Yan Qiao, Shichang Zhang, Li Xu, Ting Bai, Yanping Hu, Hongping Zhang, Wendong Zhang, Guojun |
author_facet | Hao, Yan Qiao, Shichang Zhang, Li Xu, Ting Bai, Yanping Hu, Hongping Zhang, Wendong Zhang, Guojun |
author_sort | Hao, Yan |
collection | PubMed |
description | Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features. |
format | Online Article Text |
id | pubmed-8236881 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82368812021-06-29 Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features Hao, Yan Qiao, Shichang Zhang, Li Xu, Ting Bai, Yanping Hu, Hongping Zhang, Wendong Zhang, Guojun Front Oncol Oncology Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features. Frontiers Media S.A. 2021-06-14 /pmc/articles/PMC8236881/ /pubmed/34195073 http://dx.doi.org/10.3389/fonc.2021.657560 Text en Copyright © 2021 Hao, Qiao, Zhang, Xu, Bai, Hu, Zhang and Zhang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Hao, Yan Qiao, Shichang Zhang, Li Xu, Ting Bai, Yanping Hu, Hongping Zhang, Wendong Zhang, Guojun Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features |
title | Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features |
title_full | Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features |
title_fullStr | Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features |
title_full_unstemmed | Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features |
title_short | Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features |
title_sort | breast cancer histopathological images recognition based on low dimensional three-channel features |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236881/ https://www.ncbi.nlm.nih.gov/pubmed/34195073 http://dx.doi.org/10.3389/fonc.2021.657560 |
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