WCSE

Leukocyte Detection in Blood Smear Image Based on Improved YOLOv3

Abstract— The classification of leukocytes is an important indicator for the detection of a variety of blood diseases in routine blood tests. Although there have been many papers studying the detection of WBCs (white blood cell) or classification independently, few papers consider them together. In this paper, we propose an end-to-end white blood cell localization and classification method based on improved YOLOv3. Firstly, we utilize the k-means clustering algorithm to generate the anchor boxes suitable for WBC. Secondly, to identify white blood cells of different sizes, we use multi-scale predictions with YOLOv3 network structure. Experimental results on both the LISC dataset [1] and a dataset of 7500[2] leukocyte smear images of five categories demonstrate that the proposed improved-YOLOv3 can achieve efficient detection performance in terms of accuracy. The recognition accuracy of the two data sets is reached respectively 96.4% and 95.5%.

Index Terms—leukocyte, detection, k-means clustering algorithm, multi-scale predictions

Jing Li, Junmin Wu
School of Software Engineering, University of Science and Technology of China, CHINA
School of Computer Science and Technology, University of Science and Technology of China, CHINA

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Cite: Jing Li, Junmin Wu , " Leukocyte Detection in Blood Smear Image Based on Improved YOLOv3 " Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp. 144-149, Shanghai, China, 19-21 June, 2020.