A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network
Histopathological image analysis plays an important role in the diagnosis and treatment of cholangiocarcinoma. This time-consuming and complex process is currently performed manually by pathologists. To reduce the burden on pathologists, this paper proposes a histopathological image classification m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1237816/full |
| _version_ | 1797741287362789376 |
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| author | Hui Zhou Jingyan Li Jue Huang Zhaoxin Yue |
| author_facet | Hui Zhou Jingyan Li Jue Huang Zhaoxin Yue |
| author_sort | Hui Zhou |
| collection | DOAJ |
| description | Histopathological image analysis plays an important role in the diagnosis and treatment of cholangiocarcinoma. This time-consuming and complex process is currently performed manually by pathologists. To reduce the burden on pathologists, this paper proposes a histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolutional neural networks. Specifically, the proposed model consists of a spatial branch and a channel branch. In the spatial branch, residual structural blocks are used to extract deep spatial features. In the channel branch, a multi-scale feature extraction module and some multi-level feature extraction modules are designed to extract channel features in order to increase the representational ability of the model. The experimental results of the Multidimensional Choledoch Database show that the proposed method performs better than other classical CNN classification methods. |
| first_indexed | 2024-03-12T14:24:38Z |
| format | Article |
| id | doaj.art-bcc6afe3be97471d980b4ceaa3809339 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-12T14:24:38Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-bcc6afe3be97471d980b4ceaa38093392023-08-18T11:15:25ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-08-011310.3389/fonc.2023.12378161237816A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural networkHui Zhou0Jingyan Li1Jue Huang2Zhaoxin Yue3Department of Network Engineering, College of Computer and Software, Nanjing Vocational University of Industry Technology, Nanjing, ChinaThe Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaDepartment of Network Engineering, College of Computer and Software, Nanjing Vocational University of Industry Technology, Nanjing, ChinaDepartment of Network Engineering, College of Computer and Software, Nanjing Vocational University of Industry Technology, Nanjing, ChinaHistopathological image analysis plays an important role in the diagnosis and treatment of cholangiocarcinoma. This time-consuming and complex process is currently performed manually by pathologists. To reduce the burden on pathologists, this paper proposes a histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolutional neural networks. Specifically, the proposed model consists of a spatial branch and a channel branch. In the spatial branch, residual structural blocks are used to extract deep spatial features. In the channel branch, a multi-scale feature extraction module and some multi-level feature extraction modules are designed to extract channel features in order to increase the representational ability of the model. The experimental results of the Multidimensional Choledoch Database show that the proposed method performs better than other classical CNN classification methods.https://www.frontiersin.org/articles/10.3389/fonc.2023.1237816/fullcholangiocarcinomahistopathological image classificationconvolution neural networkmultiscalefeature fusionfeature reuse |
| spellingShingle | Hui Zhou Jingyan Li Jue Huang Zhaoxin Yue A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network Frontiers in Oncology cholangiocarcinoma histopathological image classification convolution neural network multiscale feature fusion feature reuse |
| title | A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network |
| title_full | A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network |
| title_fullStr | A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network |
| title_full_unstemmed | A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network |
| title_short | A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network |
| title_sort | histopathological image classification method for cholangiocarcinoma based on spatial channel feature fusion convolution neural network |
| topic | cholangiocarcinoma histopathological image classification convolution neural network multiscale feature fusion feature reuse |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1237816/full |
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