Blood cell detection algorithm based on the YOLOv8-EOC framework

Traditional blood cell detection methods are limited by cumbersome procedures, subjective judgment, technical constraints, and difficulties in data processing and interpretation. These limitations lead to low detection efficiency, high error risk, and issues such as misdetection and missed detection. To address these problems, we propose a YOLOv8-EOC blood cell detection algorithm, optimized and improved based on the YOLOv8 framework. This algorithm designs a novel C2f_EMA module by combining the C2f and EMA mechanisms within the residual connections of the backbone network, thereby enhancing detection accuracy and feature extraction capabilities. Additionally, by replacing some convolution modules with OMNI-DIMENSIONAL DYNAMIC CONVOLUTION (ODConv), the algorithm improves feature extraction capabilities, particularly in overlapping blood cell regions. Finally, by incorporating the coordinate attention (CA) mechanism in the neck network, the algorithm enhances the recognition accuracy of small target platelets in the dataset. Extensive experimental results on the BCCD dataset demonstrate that compared to YOLOv8n, this algorithm improves the average precision of detecting three types of blood cells by 2.7%. Moreover, with only 3.06M parameters and a computational load of 7.9 GFLOPs, the detection speed and accuracy of this algorithm surpass traditional methods and other advanced algorithms. The experimental results indicate that this algorithm has high practicality for blood cell detection and meets the deployment requirements for low computational power while ensuring high accuracy