关键词: 血液检验, 血细胞计数, 深度学习, 目标检测, YOLO

Abstract:

Blood cells detection and counting are important task of blood examination. However, a large number of  cell microscopic image has various shapes, small object and complicated background, it is still a very challenge task to recognize the blood cell automatically. In order to detect the small object from complicated background more effective, a novel YOLO-Dense network model is proposed based on one-stage deep detection framework YOLO. Firstly, the proposed model uses [K]-means to cluster the size of anchor box, three different sizes of anchor box for potential detection object are obtained, then the residual network module and multiple-scale module based on feature pyramids are introduced to improve the recognition accuracy of small object. Further, the gradient dispersion and explosion for deep network training can be solved effectively through the skip-connection of the residual module. Finally, by integrating the dense-network module in the proposed model, the network inference speed is further improved. The experimental resuls show that the mAP and detection time of YOLO-Dense are 0.86 and 24.9 ms respectively. Comparing with Faster R-CNN and original YOLO algorithm, the proposed model has achieved best performance for blood cell detection.

Key words: blood examination, cell count, deep learning, object detection, YOLO