Two-stage White Blood Cells Detection Combined with Semi-supervised Classification

The classification and statistics of white blood cells (WBCs) are critical steps in the microscopic examination of blood smears. Traditional manual microscopy methods are time-consuming and labor-intensive, while machine learning-based automated detection approaches require a substantial amount of labeled data for model training, leading to high costs. To address this issue, this paper proposes a two-stage semi-supervised deep learning method for WBC detection. In the first stage, a region proposal network (RPN) with ResNet50 as the backbone is employed for the localization and segmentation of white blood cell images. In the second stage, a semi-supervised learning framework is utilized to train the WBC classifier. The model is trained and tested using 1,510 labeled blood cell microscopy images with WBC localization boxes. The proposed semi-supervised model achieves a classification accuracy of 86%, which is 3.2% higher than that of the fully supervised model. Furthermore, this two-stage model is compared with two end-to-end models, FasterRCNN and RetinaNet. The results demonstrate that the proposed two-stage model achieves an accuracy of 83.7% and a recall of 85.1% in detection tasks, both exceeding those of the FasterRCNN and RetinaNet models. Compared to a one-stage WBC detection model, the two-stage detection method allows for more thorough training of the WBC classifier, thereby enhancing overall detection performance.