BloodCell-YOLO: Efficient Detection of Blood Cell Types Using Modified YOLOv8 with GhostBottleneck and C3Ghost Modules

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