SER-DC YOLO for the Detection of Abnormal Cervical Cells
Due to the complex content of Thin Prep Cytology Test ( TCT) images of cervical cell samples with rich and diverse background colors and a certain degree of natural variation of cervical cells among different women，this poses a great difficulty in the detection of abnormal cervical cells....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2024-02-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2301 |
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| Summary: | Due to the complex content of Thin Prep Cytology Test ( TCT) images of cervical cell samples with rich and diverse background colors and a certain degree of natural variation of cervical cells among different women，this poses a great difficulty in the detection of abnormal cervical cells. To solve this challenge，a target detection network called SE-ResNet-Deformable Convolution You Only Look Once( SER-DC YOLO) is proposed. The network incorporates the attention mechanism in YOLOv5s Backbone，adds the SE-ResNet module ，then improves the network structure of the SPP layer and replaces the normal convolution with deformable convolution，and finally modifies the loss calculation function of the bounding box by replacing the Generalized Intersection over Union ( GIoU) to α-IOU Loss. Experiments show that the network improves recall by 19. 94% ，precision by 3. 52% ，and average precision by 7. 19% on the cervical image dataset compared with the YOLOv5 network. Link to related code: https: / /github. com / sleepLion99 / SER-DC_YOLO. |
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| ISSN: | 1007-2683 |