Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network

Abstract

The correct classification of white blood cell subtypes is critical in the diagnosis of blood disease. However, the performance of classical computer vision-based classification methods is heavily dependent on the features that should be carefully designed by trial and error. The machine learning-based classifier outperforms the traditional classifiers but suffers from sample labeling, which is labor intensive and time consuming. This paper presents a semi-supervised convolutional neural network that can maintain a similarly high accuracy of classification as deep learning approaches with only 10% labeled data or less. A Visual Geometry Group Net (VGGNet) model was pre-trained with a small amount of labeled data and then used to predict unlabeled data. After implementing entropy filtering and confidence filtering processes, highquality pseudo label data were obtained and served as input for the final mean teacher model training. The proposed methodology was validated on a dataset of 9069 synthetic images that correspond to five different subtypes of white blood cells. The model yielded an overall average accuracy of 94.4% with only 500 labeled samples, which is slightly lower than that of the fully supervised model with 9069 labeled samples (97.9%) but much higher than that of the fully supervised model with 500 labeled samples (86.5%). With such results, the proposed model demonstrates promising prospects for developing clinically useful solutions that are able to detect white blood cells based on blood cell images.INDEX TERMS White blood cell classification, medical imaging, deep learning, semi-supervision.