BloodCell-YOLO: Efficient Detection of Blood Cell Types Using Modified YOLOv8 with GhostBottleneck and C3Ghost Modules
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Background: Diagnosing many medical ailments, including infections, immunological problems, and hematological diseases, is a process that depends on precise as well as quick identification of blood cell. Conventional methods for blood cell identification may include skilled pathologists visually inspecting the cell under a microscope, which is a time-consuming choreography. This method is not appropriate for processing vast amounts of data, because the process is time-consuming and is prone to human mistakes.
Objective: This study aimed to improve YOLOv8 architecture, offering a more efficient and simplified model for blood cell identification. In addition, the main objective of the analysis was to reduce computational load as well as amount of parameters and still maintaining or improving detection performance.
Methods: GhostBottleneck and C3Ghost modules used in the study were included in the head and backbone of YOLOv8 architecture for improvement. All versions of YOLOv8 was subjected to the changes including n, s, m, l, and x. During the analysis, the efficacy of the recommended method was evaluated using a dataset of seven kinds of blood, namely basophil, eosinophil, lymphocyte, monocyte, neutrophil, platelets, and red blood cells (RBCs). The analysis also tested the proposed method on the well-known Blood Cell Count and Detection (BCCD) dataset, which was a common benchmark in this field, for comparing the performance. Performance of the model relating to past studies was assessed through this process.
Results: The investigation used GhostBottleneck and C3Ghost modules to reduce GFLOPS by 45.56% and the number of parameters by 76.55%. Mean average precision (mAP50) of 0.984 was achieved using recommended method. Additionally, on BCCD, the method scored 0.94 on New Cell Dataset.
Conclusion: Modifications performed to YOLOv8 design significantly increased its blood cell detection efficiency and effectiveness. The improvements showed that the changed model was suitable for real-time use in settings with constrained resources.
Keywords: Blood Cell Detection, C3Ghost, Ghostbottleneck, YOLOv8
A. Engert et al., “The European Hematology Association Roadmap for European Hematology Research: a consensus document,” Haematologica, vol. 101, no. 2, pp. 115–208, Feb. 2016, doi: 10.3324/haematol.2015.136739.
A. Gupta et al., “Deep Learning in Image Cytometry: A Review,” Cytometry A, vol. 95, no. 4, pp. 366–380, Apr. 2019, doi: 10.1002/cyto.a.23701.
K. Almezhghwi and S. Serte, “Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network,” Comput. Intell. Neurosci., vol. 2020, pp. 1–12, Jul. 2020, doi: 10.1155/2020/6490479.
D. Ryu et al., “Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning,” BME Front., vol. 2021, p. 9893804, Jan. 2021, doi: 10.34133/2021/9893804.
B. Vinoth Kumar, S. Abirami, R. J. Bharathi Lakshmi, R. Lohitha, and R. B. Udhaya, “Detection and Content Retrieval of Object in an Image using YOLO,” IOP Conf. Ser. Mater. Sci. Eng., vol. 590, no. 1, p. 012062, Oct. 2019, doi: 10.1088/1757-899X/590/1/012062.
P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Comput. Sci., vol. 199, pp. 1066–1073, 2022, doi: 10.1016/j.procs.2022.01.135.
A. Vijayakumar and S. Vairavasundaram, “YOLO-based Object Detection Models: A Review and its Applications,” Multimed. Tools Appl., Mar. 2024, doi: 10.1007/s11042-024-18872-y.
M. Hussain, “YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection,” Machines, vol. 11, no. 7, p. 677, Jun. 2023, doi: 10.3390/machines11070677.
Y. Li, Q. Fan, H. Huang, Z. Han, and Q. Gu, “A Modified YOLOv8 Detection Network for UAV Aerial Image Recognition,” Drones, vol. 7, no. 5, p. 304, May 2023, doi: 10.3390/drones7050304.
S. Liu, T. Fang, J. Han, and D. Xiao, “Approach state detection of lightweight steel sections based on improved YOLOv8-pose,” in 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL), Zhuhai, China: IEEE, Apr. 2024, pp. 1248–1256. doi: 10.1109/CVIDL62147.2024.10603545.
G. Yang, J. Wang, Z. Nie, H. Yang, and S. Yu, “A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention,” Agronomy, vol. 13, no. 7, p. 1824, Jul. 2023, doi: 10.3390/agronomy13071824.
Z. Liu, R. M. Rasika D. Abeyrathna, R. Mulya Sampurno, V. Massaki Nakaguchi, and T. Ahamed, “Faster-YOLO-AP: A lightweight apple detection algorithm based on improved YOLOv8 with a new efficient PDWConv in orchard,” Comput. Electron. Agric., vol. 223, p. 109118, Aug. 2024, doi: 10.1016/j.compag.2024.109118.
K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, “GhostNet: More Features from Cheap Operations,” Mar. 13, 2020, arXiv: arXiv:1911.11907. Accessed: Jul. 07, 2024. [Online]. Available: http://arxiv.org/abs/1911.11907
Y. Mao, H. Zhang, W. Wu, X. Gao, Z. Lin, and J. Lin, “DWS-YOLO: A Lightweight Detector for Blood Cell Detection,” Appl. Artif. Intell., vol. 38, no. 1, p. 2318673, Dec. 2024, doi: 10.1080/08839514.2024.2318673.
W. Gu and K. Sun, “AYOLOv5: Improved YOLOv5 based on attention mechanism for blood cell detection,” Biomed. Signal Process. Control, vol. 88, p. 105034, Feb. 2024, doi: 10.1016/j.bspc.2023.105034.
Y. He, “Automatic Blood Cell Detection Based on Advanced YOLOv5s Network,” IEEE Access, pp. 1–1, 2024, doi: 10.1109/ACCESS.2024.3360142.
F. Xu, X. Li, H. Yang, Y. Wang, and W. Xiang, “TE-YOLOF: Tiny and efficient YOLOF for blood cell detection,” Biomed. Signal Process. Control, vol. 73, p. 103416, Mar. 2022, doi: 10.1016/j.bspc.2021.103416.
A. Shakarami, M. B. Menhaj, A. Mahdavi-Hormat, and H. Tarrah, “A fast and yet efficient YOLOv3 for blood cell detection,” Biomed. Signal Process. Control, vol. 66, p. 102495, Apr. 2021, doi: 10.1016/j.bspc.2021.102495.
C. Liu, D. Li, and P. Huang, “ISE-YOLO: Improved Squeeze-and-Excitation Attention Module based YOLO for Blood Cells Detection,” in 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA: IEEE, Dec. 2021, pp. 3911–3916. doi: 10.1109/BigData52589.2021.9672069.
M. MIT, “Blood Cell Count and Detection.” Accessed: Jan. 06, 2024. [Online]. Available: https://public.roboflow.com/object-detection/bccd
roboflow roboflow, “New Cells Dataset.” Accessed: Jul. 31, 2024. [Online]. Available: https://universe.roboflow.com/hhh-r05so/new-cells
M. Sohan, T. Sai Ram, and Ch. V. Rami Reddy, “A Review on YOLOv8 and Its Advancements,” in Data Intelligence and Cognitive Informatics, I. J. Jacob, S. Piramuthu, and P. Falkowski-Gilski, Eds., in Algorithms for Intelligent Systems. , Singapore: Springer Nature Singapore, 2024, pp. 529–545. doi: 10.1007/978-981-99-7962-2_39.
J. Terven, D.-M. Córdova-Esparza, and J.-A. Romero-González, “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Mach. Learn. Knowl. Extr., vol. 5, no. 4, pp. 1680–1716, Nov. 2023, doi: 10.3390/make5040083.
J. Xu, H. Yang, Z. Wan, H. Mu, D. Qi, and S. Han, “Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm Module,” IEEE Access, vol. 11, pp. 105281–105287, 2023, doi: 10.1109/ACCESS.2023.3303890.
C. Gao et al., “A fast and lightweight detection model for wheat fusarium head blight spikes in natural environments,” Comput. Electron. Agric., vol. 216, p. 108484, Jan. 2024, doi: 10.1016/j.compag.2023.108484.
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