An automatic cervical cell classification model based on improved DenseNet121
Abstract The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87953-1 |
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| author | Yue Zhang Chunyu Ning Wenjing Yang |
| author_facet | Yue Zhang Chunyu Ning Wenjing Yang |
| author_sort | Yue Zhang |
| collection | DOAJ |
| description | Abstract The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network. Firstly, the SE module is embedded in DenseNet121 to increase the model’s focus on the nucleus region, which contains important diagnostic information, and reduce the focus on redundant information. Secondly, the sizes of the convolutional kernel and pooling window of the Stem layer are adjusted to adapt to the characteristics of the cervical cell images, so that the model can extract the local detailed information more effectively. Finally, the Atrous Dense Block (ADB) is constructed, and four ADB modules are integrated into DenseNet121 to enable the model to acquire global and local salient feature information. The accuracy of A2SDNet121 for two and seven-classification tasks on the Herlev dataset is 99.75% and 99.14%, respectively. The accuracy for two, three, and five-classification tasks on the SIPaKMeD dataset reaches 99.55%, 99.75% and 99.22%, respectively. Compared with other state-of-the-art algorithms, the A2SDNet121 model performs better in the multi-classification task of cervical cells, which can significantly improve the accuracy and efficiency of cervical cancer screening. |
| format | Article |
| id | doaj-art-d850edff14b843968aca7ec92aa8c9e7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d850edff14b843968aca7ec92aa8c9e72025-01-26T12:32:43ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-87953-1An automatic cervical cell classification model based on improved DenseNet121Yue Zhang0Chunyu Ning1Wenjing Yang2Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and TechnologyDepartment of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and TechnologyDepartment of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and TechnologyAbstract The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network. Firstly, the SE module is embedded in DenseNet121 to increase the model’s focus on the nucleus region, which contains important diagnostic information, and reduce the focus on redundant information. Secondly, the sizes of the convolutional kernel and pooling window of the Stem layer are adjusted to adapt to the characteristics of the cervical cell images, so that the model can extract the local detailed information more effectively. Finally, the Atrous Dense Block (ADB) is constructed, and four ADB modules are integrated into DenseNet121 to enable the model to acquire global and local salient feature information. The accuracy of A2SDNet121 for two and seven-classification tasks on the Herlev dataset is 99.75% and 99.14%, respectively. The accuracy for two, three, and five-classification tasks on the SIPaKMeD dataset reaches 99.55%, 99.75% and 99.22%, respectively. Compared with other state-of-the-art algorithms, the A2SDNet121 model performs better in the multi-classification task of cervical cells, which can significantly improve the accuracy and efficiency of cervical cancer screening.https://doi.org/10.1038/s41598-025-87953-1Cervical cellsConvolutional neural networkImage classificationMulti-scale featuresAttention mechanism |
| spellingShingle | Yue Zhang Chunyu Ning Wenjing Yang An automatic cervical cell classification model based on improved DenseNet121 Scientific Reports Cervical cells Convolutional neural network Image classification Multi-scale features Attention mechanism |
| title | An automatic cervical cell classification model based on improved DenseNet121 |
| title_full | An automatic cervical cell classification model based on improved DenseNet121 |
| title_fullStr | An automatic cervical cell classification model based on improved DenseNet121 |
| title_full_unstemmed | An automatic cervical cell classification model based on improved DenseNet121 |
| title_short | An automatic cervical cell classification model based on improved DenseNet121 |
| title_sort | automatic cervical cell classification model based on improved densenet121 |
| topic | Cervical cells Convolutional neural network Image classification Multi-scale features Attention mechanism |
| url | https://doi.org/10.1038/s41598-025-87953-1 |
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