# Parameter-Reduced YOLOv8n with GhostConv and C3Ghost for Automated Blood Cell Detection

## TL;DR

This paper improves the YOLOv8n model for blood cell detection by reducing parameters while maintaining accuracy, making it suitable for real-time medical diagnostics.

## Contribution

The novel integration of GhostConv and C3Ghost modules in YOLOv8n reduces model complexity without significant loss in detection performance.

## Key findings

- The baseline model achieved an mAP@0.5 of 0.9043 with 3.01 M parameters.
- The combined model reduced parameters to 1.71 M while maintaining an mAP@0.5 of 0.9001.
- The improved model is suitable for real-time blood cell analysis in automated laboratory systems.

## Abstract

Accurate detection of blood cells in microscopic images plays a crucial role in automated hematological analysis and clinical diagnosis. Herein, we proposed an improved YOLOv8n-based model for efficient and precise detection of red blood cells (RBCs), white blood cells (WBCs), and platelets in the BCCD dataset. The baseline YOLOv8n framework was enhanced by integrating GhostConv and C3Ghost modules to reduce model complexity while maintaining high detection performance. A series of ablation experiments were conducted to evaluate the individual and combined effects of these modules on model accuracy and computational efficiency. Experimental results demonstrated that the baseline model achieved an mAP@0.5 of 0.9043 with 3.01 M parameters. After incorporating GhostConv, the model maintained comparable accuracy (mAP@0.5 = 0.9040) with a reduction in parameters to 2.73 M. The C3Ghost integration further decreased parameters to 1.99 M with an mAP@0.5 of 0.8973. The combined model achieved an optimal balance between accuracy (mAP@0.5 = 0.9001) and compactness (1.71 M parameters). Results indicate that the improved YOLOv8n can effectively enhance detection efficiency without sacrificing precision. The proposed lightweight detection framework provides a promising solution for real-time blood cell analysis. Its high accuracy, reduced computational load, and strong generalization ability make it suitable for integration into automated laboratory systems, facilitating rapid and intelligent medical diagnostics in hematology and related biomedical applications.