Improved detection performance in blood cell count by an attention-guided deep learning method

Abstract

Blood cell count plays an important role in the field of clinical medical diagnosis. To effectively automate the counting of blood cells, recently, the deep-learning-based detection method represented by the YOLO has been proposed and used successfully. Nevertheless, the YOLO detection method has difficulties in insufficient positioning of the bounding boxes and in distinguishing overlapping objects. To overcome the limitations, we propose a new deep-learning-based method, termed Attention-YOLO, which is achieved by adding the channel attention mechanism and the spatial attention mechanism to the feature extraction network. By using the filtered and weighted feature vector to replace the original feature vector for residual fusion, Attention-YOLO can help the network to improve the detection accuracy. The experimental results indicate that compared to the standard YOLO network, the Attention-YOLO can achieve a better detection performance in blood cell count without introducing too many additional parameters, where the recognition accuracy of cells (RBCs, WBCs, and platelets) has an improvement of 6.70%, 2.13%, and 10.44%, respectively, and the mean Average Precision (mAP) has an improvement of 7.10%.