A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological I...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9091012/ |
| _version_ | 1818874492564275200 |
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| author | Xiaomin Zhou Chen Li Md Mamunur Rahaman Yudong Yao Shiliang Ai Changhao Sun Qian Wang Yong Zhang Mo Li Xiaoyan Li Tao Jiang Dan Xue Shouliang Qi Yueyang Teng |
| author_facet | Xiaomin Zhou Chen Li Md Mamunur Rahaman Yudong Yao Shiliang Ai Changhao Sun Qian Wang Yong Zhang Mo Li Xiaoyan Li Tao Jiang Dan Xue Shouliang Qi Yueyang Teng |
| author_sort | Xiaomin Zhou |
| collection | DOAJ |
| description | Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers. |
| first_indexed | 2024-12-19T13:11:28Z |
| format | Article |
| id | doaj.art-9910e9f983b444798f2e06d69b9ed16c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:11:28Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9910e9f983b444798f2e06d69b9ed16c2022-12-21T20:19:55ZengIEEEIEEE Access2169-35362020-01-018909319095610.1109/ACCESS.2020.29937889091012A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural NetworksXiaomin Zhou0https://orcid.org/0000-0002-1741-8249Chen Li1https://orcid.org/0000-0003-1545-8885Md Mamunur Rahaman2https://orcid.org/0000-0003-2268-2092Yudong Yao3https://orcid.org/0000-0003-3868-0593Shiliang Ai4https://orcid.org/0000-0003-4133-3269Changhao Sun5https://orcid.org/0000-0001-9087-0349Qian Wang6Yong Zhang7Mo Li8Xiaoyan Li9https://orcid.org/0000-0002-9806-1850Tao Jiang10https://orcid.org/0000-0001-8296-3027Dan Xue11Shouliang Qi12https://orcid.org/0000-0003-0977-1939Yueyang Teng13Microscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang, ChinaDepartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USAMicroscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang, ChinaCancer Hospital of China Medical University, Liaoning Hospital and Institute, Shenyang, ChinaCancer Hospital of China Medical University, Liaoning Hospital and Institute, Shenyang, ChinaCancer Hospital of China Medical University, Liaoning Hospital and Institute, Shenyang, ChinaCancer Hospital of China Medical University, Liaoning Hospital and Institute, Shenyang, ChinaControl Engineering College, Chengdu University of Information Technology, Chengdu, ChinaMicroscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang, ChinaBreast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.https://ieeexplore.ieee.org/document/9091012/Breast cancerhistopathologyconvolutional neural networksdeep learningimage segmentationimage classification |
| spellingShingle | Xiaomin Zhou Chen Li Md Mamunur Rahaman Yudong Yao Shiliang Ai Changhao Sun Qian Wang Yong Zhang Mo Li Xiaoyan Li Tao Jiang Dan Xue Shouliang Qi Yueyang Teng A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks IEEE Access Breast cancer histopathology convolutional neural networks deep learning image segmentation image classification |
| title | A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks |
| title_full | A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks |
| title_fullStr | A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks |
| title_full_unstemmed | A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks |
| title_short | A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks |
| title_sort | comprehensive review for breast histopathology image analysis using classical and deep neural networks |
| topic | Breast cancer histopathology convolutional neural networks deep learning image segmentation image classification |
| url | https://ieeexplore.ieee.org/document/9091012/ |
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