Diagnostic of breast tumors based on improved EfficientNet
Breast tumors adversely affect the holistic well-being of women. Histopathological images are a critical substantiation for doctors to diagnose breast tumor types. The structure of various types of tumor cells exhibits significant correlations, thereby posing challenges to the diagnosis using conven...
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| Format: | Article |
| Language: | zho |
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POSTS&TELECOM PRESS Co., LTD
2023-12-01
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| Series: | 智能科学与技术学报 |
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| Online Access: | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202343 |
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| _version_ | 1846171186337153024 |
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| author | FANG Zhenqi LI Xue MO Hong |
| author_facet | FANG Zhenqi LI Xue MO Hong |
| author_sort | FANG Zhenqi |
| collection | DOAJ |
| description | Breast tumors adversely affect the holistic well-being of women. Histopathological images are a critical substantiation for doctors to diagnose breast tumor types. The structure of various types of tumor cells exhibits significant correlations, thereby posing challenges to the diagnosis using conventional methods. In this work, the enhanced EfficientNet was employed for the diagnosis of breast tumors, which enabled the network model to learn the features of the disease automatically and improve the accuracy of the diagnosis of breast tumor types. Firstly, the convolutional block attention module was used to extract effective features. Secondly, the group convolution and channel shuffle operations were introduced to improve the feature representation ability of the model. Thirdly, the Hard-Swish activation function was applied to improve the convergence speed of the model. Finally, Experiments showed that the improved EfficientNet network achieved 98.4% accuracy in eight classifications on the BreakHis dataset, which was expected to act a decision aid tool in breast tumor diagnostic research. |
| format | Article |
| id | doaj-art-50dca2eb77bd4ddf9cc01b3b121b56b8 |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-50dca2eb77bd4ddf9cc01b3b121b56b82024-11-11T06:50:46ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522023-12-01550551448703294Diagnostic of breast tumors based on improved EfficientNetFANG ZhenqiLI XueMO HongBreast tumors adversely affect the holistic well-being of women. Histopathological images are a critical substantiation for doctors to diagnose breast tumor types. The structure of various types of tumor cells exhibits significant correlations, thereby posing challenges to the diagnosis using conventional methods. In this work, the enhanced EfficientNet was employed for the diagnosis of breast tumors, which enabled the network model to learn the features of the disease automatically and improve the accuracy of the diagnosis of breast tumor types. Firstly, the convolutional block attention module was used to extract effective features. Secondly, the group convolution and channel shuffle operations were introduced to improve the feature representation ability of the model. Thirdly, the Hard-Swish activation function was applied to improve the convergence speed of the model. Finally, Experiments showed that the improved EfficientNet network achieved 98.4% accuracy in eight classifications on the BreakHis dataset, which was expected to act a decision aid tool in breast tumor diagnostic research.http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202343breast tumor;EfficientNet;image classification;convolutional neural network |
| spellingShingle | FANG Zhenqi LI Xue MO Hong Diagnostic of breast tumors based on improved EfficientNet 智能科学与技术学报 breast tumor;EfficientNet;image classification;convolutional neural network |
| title | Diagnostic of breast tumors based on improved EfficientNet |
| title_full | Diagnostic of breast tumors based on improved EfficientNet |
| title_fullStr | Diagnostic of breast tumors based on improved EfficientNet |
| title_full_unstemmed | Diagnostic of breast tumors based on improved EfficientNet |
| title_short | Diagnostic of breast tumors based on improved EfficientNet |
| title_sort | diagnostic of breast tumors based on improved efficientnet |
| topic | breast tumor;EfficientNet;image classification;convolutional neural network |
| url | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202343 |
| work_keys_str_mv | AT fangzhenqi diagnosticofbreasttumorsbasedonimprovedefficientnet AT lixue diagnosticofbreasttumorsbasedonimprovedefficientnet AT mohong diagnosticofbreasttumorsbasedonimprovedefficientnet |