SW-YOLO: Improved YOLOv5s Algorithm for Blood Cell Detection
Pages 161 - 172
Abstract
This paper proposes an improved target detection algorithm SW-YOLO based on the YOLOv5s framework to solve the problems of low detection accuracy, wrong detection and missed detection in blood cell detection tasks. To begin with, the end of the backbone network is fused with Swin Transformer to improve network feature extraction. Next, since blood cells are mostly small and medium-sized targets, resulting in poor detection of large cells, the network layer that identifies large cells is removed. In addition, the normal convolution in the PANet network is replaced with depth-separable convolution during the feature fusion process to ensure the accuracy and real-time detection while having better detection results for small targets. At last, the loss function of the prediction layer uses EIOU to solve the positive and negative sample imbalance problem of CIOU. Compared with existing target detection algorithms such as Faster-RCNN, YOLOv4 and YOLOv5s, SW-YOLO improves to 99.5%, 95.3% and 93.3% mAP on the blood cell dataset BCCD for white blood cells, red blood cells and platelets respectively. The experimental results are eximious and the algorithm is highly practical for blood cell detection.
References
[1]
Chen Y Study on the value of blood smear analysis in routine blood tests China Pharm. Guide 2018 16 1 118-119
[2]
Chan L, Laverty D, and Smith T Accurate measurement of peripheral blood mononuclear cell concentration using image cytometry to eliminate RBC-induced counting error J. Immunol. Methods 2013 388 1 25-32
[3]
Lehmann TM, Guld MO, and Thies C Content-based image retrieval in medical applications Methods Inf. Med. 2004 43 4 354-361
[4]
Ren SQ, He K, and Girshick R Faster R-CNN: towards real-time object detection with region proposal networks IEEE Trans. Pattern Anal. Mach. Intell. 2017 39 6 1137-1149
[5]
Chen DH, Sun SR, and Wang YC Research on improved SSD algorithm for small target detection Sensors Microsyst. 2023 42 3 65-68
[6]
Liu JC, Li XF, and Liu AX Improved RetinaNet for UAV small target detection Sci. Technol. Eng. 2023 23 1 274-282
[7]
Yan WJ and Dai JH Traffic sign recognition based on improved YOLOv3 J. Wuhan Eng. Vocation. Technol. Coll. 2023 35 1 31-35
[8]
Chen YF, Yan CC, and Zhou C Improved YOLOv4-based vehicle detection for autonomous driving scenarios Autom. Instrum. 2023 38 1 59-63
[9]
Zheng CW and Lin H A YOLOv5 helmet wearing detection method based on Swin Transformer Comput. Meas. Control 2023 31 3 15-21
[10]
Zhang YF, Ren W, and Zhang Z Focal and efficient IOU loss for accurate bounding box regression Neurocomputing 2022 506 146-157
[11]
Ouyang D, Huang H, and Li J Improved yolov5s model for aerial image target detection algorithm Fujian Comput. 2023 39 5 7-15
[12]
He, K., Zhang, X., Ren, S.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
[13]
Shah SAR, Wu W, and Lu Q AmoebaNet: an SDN-enabled network service for big data science J. Netw. Comput. Appl. 2018 119 70-82
[14]
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
[15]
Lin, T.Y., Dollar, P., Girshick, R.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
[16]
Liu, S., Qi, L., Qin, H.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)
[17]
https://github.com/Shenggan/BCCD_Dataset. Accessed 24 Feb 2018
[18]
Xu F, Li X, and Yang H TE-YOLOF: tiny and efficient YOLOF for blood cell detection Biomed. Signal Process. Control 2022 73 103416
[19]
Liu, C., Li, D., Huang, P.: ISE-YOLO: Improved squeeze-and-excitation attention module based YOLO for blood cells detection. In: 2021 IEEE International Conference on Big Data, pp. 3911–3916 (2021)
[20]
Lung Nodule Analysis 2016. https://luna16.grand-challenge.org. Accessed 20 Oct 2020
[21]
Huang SA Traffic sign detection based on improved YOLO model Sci. Technol. Innov. 2021 18 194-196
Recommendations
UAV small target detection algorithm based on an improved YOLOv5s model
AbstractThe targets of UAV target detection are usually small targets, and the backgrounds are complex. In this work, aiming at the problem that small targets are easy to be missed or misdetected during the UAV detection, an improved YOLOv5s_MSES target ...
Highlights- Improved YOLOv5s_MSES algorithm for small target detection in UAV aerial photography.
- Introduction of Small target detection module STD to enhance detection of small targets.
- Multi-scale feature fusion module for improved accuracy ...
White blood cell count and chronic obstructive pulmonary disease: A Mendelian Randomization study
AbstractBlood leukocyte counts (e.g., eosinophil count) are important biomarkers for the onset, classification, and exacerbation of chronic obstructive pulmonary disease (COPD). The causal relationships between them are necessary for the ...
Highlights- Elevated blood eosinophil count increases COPD risk, independently of asthma.
- ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Jul 2023
646 pages
ISBN:978-981-99-6488-8
DOI:10.1007/978-981-99-6489-5
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 11 October 2023
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Jan 2025