Deep overparameterized blood cell detection algorithm utilizing hybrid attention mechanisms_Journal of Biomedical Engineering

Authors：

 ZHU Shuo ¹ , ZHANG Xukang ² , WANG Zongyang ³ , JIANG Rui ¹ , LIU Zhengda ¹

1. Jiangsu Province Engineering Research Center of Photonic Devices and System Integration for Communication Sensing Convergence, Wuxi University, Wuxi, Jiangsu 214105, P. R. China;
2. College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China;
3. Wuxi Xi Yuan Technology by the Company, Wuxi, Jiangsu 214000, P. R. China;

Corresponding author：

ZHU Shuo, Email: zshuo2011@163.com

Keywords：

Blood cell detection; Attention mechanism; Object detection; Overparameterization; Deep learning

DOI：

10.7507/1001-5515.202412057

Video：

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To address the challenges in blood cell recognition caused by diverse morphology, dense distribution, and the abundance of small target information, this paper proposes a blood cell detection algorithm - the "You Only Look Once" model based on hybrid mixing attention and deep over-parameters (HADO-YOLO). First, a hybrid attention mechanism is introduced into the backbone network to enhance the model's sensitivity to detailed features. Second, the standard convolution layers with downsampling in the neck network are replaced with deep over-parameterized convolutions to expand the receptive field and improve feature representation. Finally, the detection head is decoupled to enhance the model's robustness for detecting abnormal cells. Experimental results on the Blood Cell Counting Dataset (BCCD) demonstrate that the HADO-YOLO algorithm achieves a mean average precision of 90.2% and a precision of 93.8%, outperforming the baseline YOLO model. Compared with existing blood cell detection methods, the proposed algorithm achieves state-of-the-art detection performance. In conclusion, HADO-YOLO offers a more efficient and accurate solution for identifying various types of blood cells, providing valuable technical support for future clinical diagnostic applications.

1.	唐晓卿, 赵晨. 外周血细胞形态学检查在血常规检验中的应用价值. 现代诊断与治疗, 2021, 32(5): 756-757.
2.	Kutlu H, Avci E, Özyurt F. White blood cells detection and classification based on regional convolutional neural networks. Med Hypotheses, 2020, 135: 109472.
3.	Reddy V H. Automatic red blood cell and white blood cell counting for telemedicine system. International Journal of Research in Advent Technology, 2014, 2(1): 294-299.
4.	Hari J,Prasad A S,Rao S K. Separation and counting of blood cells using geometrical features and distance transformed watershed//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Washington: IEEE Computer Society, 2014: 77-82.
5.	Bise R, Sato Y. Cell detection from redundant candidate regions under nonoverlapping constraints. IEEE Trans Med Imaging, 2015, 34(7): 1417-1427.
6.	Zhang W, Wang S, Thachan S, et al. Deconv R-CNN for small object detection on remote sensing images//Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Washington: IEEE, 2018: 2483-2486.
7.	张幸, 赵文仓, 王旭. 基于自适配归一化的改进Mask Scoring R-CNN. 电子测量技术, 2020, 43(6): 93-98.
8.	翟永杰, 王璐瑶, 郭聪彬. 基于Faster R-CNN的复杂背景下绝缘子目标检测. 电子测量技术, 2023, 46(16): 187-194.
9.	Jiang P, Ergu D, Liu F, et al. A review of Yolo algorithm developments. Procedia Computer Science, 2022, 199: 1066-1073.
10.	Liu W, Anguelov D, Ehan D, et al. SSD: single shot multibox detector// Proceedings of the 14th European Conference on Computer Vision (ECCV 2016), Amsterdam: Springer, 2016: 21-37.
11.	徐晓涛, 孙亚东, 章军. 基于YOLO框架的血细胞自动计数研究. 计算机工程与应用, 2020, 56(14): 98-103.
12.	Gao H, Zhuang L, Maaten L V D, et al. Densely connected convolutional networks//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Washington: IEEE Computer Society, 2017: 2261-2269.
13.	王榆锋, 李大海. 改进YOLO框架的血细胞检测算法. 计算机工程与应用, 2022, 58(12): 191-198.
14.	Jiang Z, Liu X, Yan Z, et al. Improved detection performance in blood cell count by an attention-guided deep learning method. OSA Continuum, 2021, 4(2): 323-333.
15.	张昊, 郑广海, 张鑫等. 改进YOLOv5框架的血细胞检测算法. 计算机系统应用, 2023, 32(5): 123-131.
16.	Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville: IEEE Computer Society, 2021: 13713-13722.
17.	Tan M, Pang R, Le Q V. Efficientdet: scalable and efficient object detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle: IEEE Computer Society, 2020: 10781-10790.
18.	Song Q, Li S, Bai Q, et al. Object detection method for grasping robot based on improved YOLOv5. Micromachines, 2021, 12(11): 1273.
19.	Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver: IEEE Computer Society, 2023: 7464-7475.
20.	Wang Y, Wang H, Xin Z. Efficient detection model of steel strip surface defects based on YOLO-V7. IEEE Access, 2022, 10: 133936-133944.
21.	Hu J, Shen L, Sun G. Squeeze-and-excitation networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City: IEEE, 2018: 7132-7141.
22.	邱晓龙. 基于深度学习的交通标志检测与识别方法研究. 北京: 华北电力大学, 2022.
23.	Yin M, Chen Z, Zhang C. A CNN-Transformer network combining CBAM for change detection in high-resolution remote sensing images. Remote Sensing, 2023, 15(9): 2406.
24.	Cao J, Li Y, Sun M, et al. DO-Conv: depthwise over-parameterized convolutional layer. IEEE Trans Image Process, 2022, 31: 3726-3736.
25.	Ge Z, Liu S, Wang F, et al. YOLOX: exceeding YOLO Series in 2021. arXiv preprint, 2021, arXiv: 2107.08430.
26.	Hu S, Zhao F, Lu H, et al. Improving YOLOv7-tiny for infrared and visible light image object detection on drones. Remote Sensing, 2023, 15(13): 3214.
27.	Ananthi P, Devi K N, Ramya T E, et al. Blood cell classification and counting in hematology through deep learning//2024 4th International Conference on Sustainable Expert Systems (ICSES), Kaski: IEEE, 2024: 813-819.
28.	Chen Q, Wang Y, Yang T, et al. You only look one-level feature//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville: IEEE Computer Society, 2021: 13039-13048.
29.	Liu C, Li D W, Huang P. ISE-YOLO: improved squeeze-and excitation attention module based YOLO for blood cellsdetection//Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando: IEEE, 2021: 3911-3916.

1. 唐晓卿, 赵晨. 外周血细胞形态学检查在血常规检验中的应用价值. 现代诊断与治疗, 2021, 32(5): 756-757.
2. Kutlu H, Avci E, Özyurt F. White blood cells detection and classification based on regional convolutional neural networks. Med Hypotheses, 2020, 135: 109472.
3. Reddy V H. Automatic red blood cell and white blood cell counting for telemedicine system. International Journal of Research in Advent Technology, 2014, 2(1): 294-299.
4. Hari J,Prasad A S,Rao S K. Separation and counting of blood cells using geometrical features and distance transformed watershed//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Washington: IEEE Computer Society, 2014: 77-82.
5. Bise R, Sato Y. Cell detection from redundant candidate regions under nonoverlapping constraints. IEEE Trans Med Imaging, 2015, 34(7): 1417-1427.
6. Zhang W, Wang S, Thachan S, et al. Deconv R-CNN for small object detection on remote sensing images//Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Washington: IEEE, 2018: 2483-2486.
7. 张幸, 赵文仓, 王旭. 基于自适配归一化的改进Mask Scoring R-CNN. 电子测量技术, 2020, 43(6): 93-98.
8. 翟永杰, 王璐瑶, 郭聪彬. 基于Faster R-CNN的复杂背景下绝缘子目标检测. 电子测量技术, 2023, 46(16): 187-194.
9. Jiang P, Ergu D, Liu F, et al. A review of Yolo algorithm developments. Procedia Computer Science, 2022, 199: 1066-1073.
10. Liu W, Anguelov D, Ehan D, et al. SSD: single shot multibox detector// Proceedings of the 14th European Conference on Computer Vision (ECCV 2016), Amsterdam: Springer, 2016: 21-37.
11. 徐晓涛, 孙亚东, 章军. 基于YOLO框架的血细胞自动计数研究. 计算机工程与应用, 2020, 56(14): 98-103.
12. Gao H, Zhuang L, Maaten L V D, et al. Densely connected convolutional networks//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Washington: IEEE Computer Society, 2017: 2261-2269.
13. 王榆锋, 李大海. 改进YOLO框架的血细胞检测算法. 计算机工程与应用, 2022, 58(12): 191-198.
14. Jiang Z, Liu X, Yan Z, et al. Improved detection performance in blood cell count by an attention-guided deep learning method. OSA Continuum, 2021, 4(2): 323-333.
15. 张昊, 郑广海, 张鑫等. 改进YOLOv5框架的血细胞检测算法. 计算机系统应用, 2023, 32(5): 123-131.
16. Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville: IEEE Computer Society, 2021: 13713-13722.
17. Tan M, Pang R, Le Q V. Efficientdet: scalable and efficient object detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle: IEEE Computer Society, 2020: 10781-10790.
18. Song Q, Li S, Bai Q, et al. Object detection method for grasping robot based on improved YOLOv5. Micromachines, 2021, 12(11): 1273.
19. Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver: IEEE Computer Society, 2023: 7464-7475.
20. Wang Y, Wang H, Xin Z. Efficient detection model of steel strip surface defects based on YOLO-V7. IEEE Access, 2022, 10: 133936-133944.
21. Hu J, Shen L, Sun G. Squeeze-and-excitation networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City: IEEE, 2018: 7132-7141.
22. 邱晓龙. 基于深度学习的交通标志检测与识别方法研究. 北京: 华北电力大学, 2022.
23. Yin M, Chen Z, Zhang C. A CNN-Transformer network combining CBAM for change detection in high-resolution remote sensing images. Remote Sensing, 2023, 15(9): 2406.
24. Cao J, Li Y, Sun M, et al. DO-Conv: depthwise over-parameterized convolutional layer. IEEE Trans Image Process, 2022, 31: 3726-3736.
25. Ge Z, Liu S, Wang F, et al. YOLOX: exceeding YOLO Series in 2021. arXiv preprint, 2021, arXiv: 2107.08430.
26. Hu S, Zhao F, Lu H, et al. Improving YOLOv7-tiny for infrared and visible light image object detection on drones. Remote Sensing, 2023, 15(13): 3214.
27. Ananthi P, Devi K N, Ramya T E, et al. Blood cell classification and counting in hematology through deep learning//2024 4th International Conference on Sustainable Expert Systems (ICSES), Kaski: IEEE, 2024: 813-819.
28. Chen Q, Wang Y, Yang T, et al. You only look one-level feature//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville: IEEE Computer Society, 2021: 13039-13048.
29. Liu C, Li D W, Huang P. ISE-YOLO: improved squeeze-and excitation attention module based YOLO for blood cellsdetection//Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando: IEEE, 2021: 3911-3916.

Journal of Biomedical Engineering

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