High-Fidelity Blood Cell Detection in Microscopy: Comparative Evaluation of YOLOv9 and Faster R-CNN Architectures

Abstract

Accurate detection of blood cells from microscopic images plays an essential role in clinical hematology, disease diagnosis, and laboratory automation. Traditional microscopic examination of peripheral blood smears requires expert pathologists and is time-consuming, especially when large volumes of samples must be analyzed. Recent developments in computer vision and deep learning have enabled automated systems capable of detecting and classifying blood cells with high accuracy. This research presents a comparative evaluation of two advanced object detection architectures—YOLOv9 and Faster R-CNN—for automated blood cell detection in microscopic images. The proposed framework aims to identify and localize three primary blood components: red blood cells (RBC), white blood cells (WBC), and platelets. The system utilizes preprocessing techniques, deep learning-based feature extraction, and bounding-box detection to accurately locate cells within smear images. Experimental evaluation is performed using annotated microscopy datasets. YOLOv9 provides faster inference speed suitable for real-time clinical applications, while Faster R-CNN demonstrates strong localization capability due to its region proposal network. Comparative results indicate that YOLOv9 achieves superior detection speed and competitive accuracy, making it suitable for automated laboratory systems. The proposed system contributes toward improving diagnostic efficiency and reducing manual workload in hematology laboratories.