Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module.
Although successful detection of malignant tumors from histopathological images largely depends on the long-term experience of radiologists, experts sometimes disagree with their decisions. Computer-aided diagnosis provides a second option for image diagnosis, which can improve the reliability of ex...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0214587 |
| _version_ | 1818403567090794496 |
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| author | Yun Jiang Li Chen Hai Zhang Xiao Xiao |
| author_facet | Yun Jiang Li Chen Hai Zhang Xiao Xiao |
| author_sort | Yun Jiang |
| collection | DOAJ |
| description | Although successful detection of malignant tumors from histopathological images largely depends on the long-term experience of radiologists, experts sometimes disagree with their decisions. Computer-aided diagnosis provides a second option for image diagnosis, which can improve the reliability of experts' decision-making. Automatic and precision classification for breast cancer histopathological image is of great importance in clinical application for identifying malignant tumors from histopathological images. Advanced convolution neural network technology has achieved great success in natural image classification, and it has been used widely in biomedical image processing. In this paper, we design a novel convolutional neural network, which includes a convolutional layer, small SE-ResNet module, and fully connected layer. We propose a small SE-ResNet module which is an improvement on the combination of residual module and Squeeze-and-Excitation block, and achieves the similar performance with fewer parameters. In addition, we propose a new learning rate scheduler which can get excellent performance without complicatedly fine-tuning the learning rate. We use our model for the automatic classification of breast cancer histology images (BreakHis dataset) into benign and malignant and eight subtypes. The results show that our model achieves the accuracy between 98.87% and 99.34% for the binary classification and achieve the accuracy between 90.66% and 93.81% for the multi-class classification. |
| first_indexed | 2024-12-14T08:26:18Z |
| format | Article |
| id | doaj.art-d98c453f34234bdabf616833c7e9de82 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T08:26:18Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d98c453f34234bdabf616833c7e9de822022-12-21T23:09:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01143e021458710.1371/journal.pone.0214587Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module.Yun JiangLi ChenHai ZhangXiao XiaoAlthough successful detection of malignant tumors from histopathological images largely depends on the long-term experience of radiologists, experts sometimes disagree with their decisions. Computer-aided diagnosis provides a second option for image diagnosis, which can improve the reliability of experts' decision-making. Automatic and precision classification for breast cancer histopathological image is of great importance in clinical application for identifying malignant tumors from histopathological images. Advanced convolution neural network technology has achieved great success in natural image classification, and it has been used widely in biomedical image processing. In this paper, we design a novel convolutional neural network, which includes a convolutional layer, small SE-ResNet module, and fully connected layer. We propose a small SE-ResNet module which is an improvement on the combination of residual module and Squeeze-and-Excitation block, and achieves the similar performance with fewer parameters. In addition, we propose a new learning rate scheduler which can get excellent performance without complicatedly fine-tuning the learning rate. We use our model for the automatic classification of breast cancer histology images (BreakHis dataset) into benign and malignant and eight subtypes. The results show that our model achieves the accuracy between 98.87% and 99.34% for the binary classification and achieve the accuracy between 90.66% and 93.81% for the multi-class classification.https://doi.org/10.1371/journal.pone.0214587 |
| spellingShingle | Yun Jiang Li Chen Hai Zhang Xiao Xiao Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS ONE |
| title | Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. |
| title_full | Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. |
| title_fullStr | Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. |
| title_full_unstemmed | Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. |
| title_short | Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. |
| title_sort | breast cancer histopathological image classification using convolutional neural networks with small se resnet module |
| url | https://doi.org/10.1371/journal.pone.0214587 |
| work_keys_str_mv | AT yunjiang breastcancerhistopathologicalimageclassificationusingconvolutionalneuralnetworkswithsmallseresnetmodule AT lichen breastcancerhistopathologicalimageclassificationusingconvolutionalneuralnetworkswithsmallseresnetmodule AT haizhang breastcancerhistopathologicalimageclassificationusingconvolutionalneuralnetworkswithsmallseresnetmodule AT xiaoxiao breastcancerhistopathologicalimageclassificationusingconvolutionalneuralnetworkswithsmallseresnetmodule |