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Improved Immunohistochemistry Active Cell Counting Method for YOLOv5s
- Source :
- BIO Web of Conferences, Vol 111, p 01020 (2024)
- Publication Year :
- 2024
- Publisher :
- EDP Sciences, 2024.
-
Abstract
- This article proposes an improved YOLOv5s counting method to address the problems of long-term manual counting of positive cells in immunohistochemical images and low consistency. First, by introducing the Triplet attention module, the model focuses on the positive cell area, reducing background interference and improving the network's ability to extract positive cell features; then, a small target detection layer is added to better utilize the semantic information of the network to improve positive cells. recognition accuracy; then, the lightweight up-sampling operator CARAFE is used to improve the quality and accuracy of up-sampling; finally, the WIoU loss function is used to replace the original GIoU of YOLOv5 to enhance model detection performance. Experimental results show that the improved model has an average accuracy of 88.4%, which is 3.1% higher than the original YOLOv5 network model. It can count positive cells quickly and accurately, reducing the workload of doctors.
- Subjects :
- Microbiology
QR1-502
Physiology
QP1-981
Zoology
QL1-991
Subjects
Details
- Language :
- English, French
- ISSN :
- 21174458
- Volume :
- 111
- Database :
- Directory of Open Access Journals
- Journal :
- BIO Web of Conferences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.83073851e4104d4fb29859b2278ea035
- Document Type :
- article
- Full Text :
- https://doi.org/10.1051/bioconf/202411101020