A Framework for Segmentation and Classification of Cervical Cells Under Long-tailed Distribution
Currently, there are two challenges in identifying abnormal cervical cells using deep learning: (1) cervical cells are diverse and cervical cell images vary from person to person. (2 ) Cervical cells show long-tailed distribution, which affects the classification accuracy of cervical cells. In this...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Harbin University of Science and Technology Publications
2023-12-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2284 |
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| Summary: | Currently, there are two challenges in identifying abnormal cervical cells using deep learning: (1) cervical cells are diverse and cervical cell images vary from person to person. (2 ) Cervical cells show long-tailed distribution, which affects the classification accuracy of cervical cells. In this paper, a deep learning-based cervical cell segmentation and classification framework is proposed. This framework first performs cell nucleus segmentation, uses U-Net as the base model for layer reduction, adds AG module, and uses ACBlock module instead of traditional standard convolutional blocks; then uses ResNeSt for coarse classification of segmented data, fuses manual features extracted based on physicians ′ experience and machine features extracted by ResNeSt network for fine classification , and uses active learning iteratively to expand the cervical cell categories and fuse the ACBlock module in the BBN model to process the long-tail data; finally, the diagnostic indexes of abnormal cells are refined and abnormal cells are screened according to the TBS diagnostic criteria and the physician ′s diagnostic experience. Experiments show that the segmentation algorithm in this paper improves 3. 52% compared with the original method, and the classification algorithm that incorporates all features improves 1. 2% . For positive patients, the accuracy of cancer cell diagnosis reached 91% . |
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| ISSN: | 1007-2683 |