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Data augmentation is reported as a useful technique to generate a large amount of image datasets from a small image dataset. The aim of this study is to clarify the effect of data augmentation for leukocyte recognition with deep learning. We performed three different data augmentation methods (rotation, scaling, and distortion) as pretreatment on the original images. The subjects of clinical assessment were 51 healthy persons. The thin-layer blood smears were prepared from peripheral blood and stained with MG. The effect of data augmentation with rotation was the only significant effective technique in AI model generation for leukocyte recognition. On contrast, the effect of data augmentation with image distortion or image scaling was poor, and accuracy improvement was limited to specific leukocyte categories. Although data augmentation is one effective method for high accuracy in AI training, we consider that a highly effective method should be selected.

The Effect of Data Augmentation in Deep Learning Approach for Peripheral Blood Leukocyte Recognition