Two-stage Detection Method for Abnormal Cluster Cervical Cells
Abnormal cell detection is a key technique for intelligent assisted diagnosis of cervical cancer, which directly affects the performance of the detection system. However, most cervical abnormal cells exist in the form of clusters. Cells adhere to each other, complex and diverse, which brings challen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2022-04-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2078 |
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| Summary: | Abnormal cell detection is a key technique for intelligent assisted diagnosis of cervical cancer, which directly affects the performance of the detection system. However, most cervical abnormal cells exist in the form of clusters. Cells adhere to each other, complex and diverse, which brings challenges to abnormal cell detection. To solve this problem, we proposed a two-stage detection method for cluster cervical abnormal cells. In the first stage, we use YOLO-v5 target detection network. The standard convolution in the network is replaced by deformable convolution. The size and location of convolution kernel can be dynamically adjusted according to the current pathological image content, so as to adapt to the shape, size and other geometric changes of cervical cells in different clusters. In the second stage, the supervised contrastive learning network is used to learn the feature differences between positive and abnormal clusters of cervical cells, so as to achieve high accuracy classification of positive and abnormal clusters of cervical cells. The experimental results show that the recall rate of cluster cervical cells reaches 89.69 %, which is 1.43 % higher than that of baseline network YOLO-v5.The classification accuracy of positive and abnormal cluster cervical cells reaches 87.81 %, which is 10.31 % higher than that of baseline network ResNet. |
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| ISSN: | 1007-2683 |