LeukoX: Leukocyte Classification Using Least Entropy Combiner (LEC) for Ensemble Learning

This brief proposes a scheme to detect and classify across four different classes of white blood cells (WBC) based on their morphological features by ensemble learning applied over multiple deep neural networks (DNN). Contrary to the DNN based WBC classification schemes, this method makes use of a novel least entropy combiner (LEC) network that effectively combines the individual classifier decisions in order to minimize the cross entropy cost. In this brief, two pretrained DNNs, namely DenseNet121 and ResNet50 are used along with a custom-designed convolutional neural network (CNN) to individually produce the class confidence scores which are fed to the proposed LEC network for the ensemble learning purpose. A dataset comprising of around 12, 500 images of four different WBC types, namely Eosinophil, Lymphocyte, Monocyte, and Neutrophil are considered for training and testing the proposed network. The proposed model yields superior performance than the individual networks. Our results demonstrate that the proposed model achieves an overall test accuracy of 96.67%, with precision and sensitivity scores of 96.73%, and 96.62% respectively and the Matthews correlation coefficient (MCC) and kappa scores being 0.9334 and 0.9550, respectively. The overall model is termed as LeukoX (X: Extended).

Automated summary

This study proposes a scheme for white blood cell classification using ensemble learning and multiple deep neural networks, utilizing a novel least entropy combiner network to effectively combine individual classifier decisions. By utilizing three deep neural networks and a custom-designed convolutional neural network, higher accuracy can be achieved on a dataset of four different types of white blood cells.