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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Harbin University of Science and Technology Publications
2022-04-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2078 |
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| author | LIANG Yi-qin ZHAO Si-qi WANG Hai-tao HE Yong-jun |
| author_facet | LIANG Yi-qin ZHAO Si-qi WANG Hai-tao HE Yong-jun |
| author_sort | LIANG Yi-qin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fa4ecc1181d4429091aa53dd916b70e5 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2022-04-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-fa4ecc1181d4429091aa53dd916b70e52025-08-20T02:52:52ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832022-04-012702768410.15938/j.jhust.2022.02.010Two-stage Detection Method for Abnormal Cluster Cervical CellsLIANG Yi-qin0ZHAO Si-qi1WANG Hai-tao2HE Yong-jun3School of Computer and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaThe First Affiliated Hospital of Harbin Medical University, Harbin, 150001, ChinaSchool of Computer and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaAbnormal 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.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2078cluster cervical cell classificationtarget detectioncontrastive learning |
| spellingShingle | LIANG Yi-qin ZHAO Si-qi WANG Hai-tao HE Yong-jun Two-stage Detection Method for Abnormal Cluster Cervical Cells Journal of Harbin University of Science and Technology cluster cervical cell classification target detection contrastive learning |
| title | Two-stage Detection Method for Abnormal Cluster Cervical Cells |
| title_full | Two-stage Detection Method for Abnormal Cluster Cervical Cells |
| title_fullStr | Two-stage Detection Method for Abnormal Cluster Cervical Cells |
| title_full_unstemmed | Two-stage Detection Method for Abnormal Cluster Cervical Cells |
| title_short | Two-stage Detection Method for Abnormal Cluster Cervical Cells |
| title_sort | two stage detection method for abnormal cluster cervical cells |
| topic | cluster cervical cell classification target detection contrastive learning |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2078 |
| work_keys_str_mv | AT liangyiqin twostagedetectionmethodforabnormalclustercervicalcells AT zhaosiqi twostagedetectionmethodforabnormalclustercervicalcells AT wanghaitao twostagedetectionmethodforabnormalclustercervicalcells AT heyongjun twostagedetectionmethodforabnormalclustercervicalcells |