Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

Abstract

White blood cells (WBCs), which are a crucial component of the immune system, help our body defend against infections and other diseases. Some diseases may cause our body to produce fewer WBCs than it requires. Therefore, WBCs are of great importance in medical imaging. Artificial intelligence-based computer systems can assist experts in analyzing WBCs. In this study, we proposed an approach for the automatic classification of WBCs into five different classes using a pre-trained model. We trained ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models with ImageNet weights. For the training, validation, and testing processes of the models, we used a public dataset containing 16,633 images with an uneven class distribution. While the ResNet-50 model achieved an accuracy of 98.79%, the VGG-19 model achieved an accuracy of 98.19%, and the MobileNet-V3-Small model achieved the highest accuracy rate at 98.86%. When examining the predictions of the MobileNet-V3-Small model, we observed that it was not affected by class dominance and was able to correctly classify even the least sampled class images in the dataset. In addition to the high accuracy achieved in the classification of WBCs using the proposed pre-trained deep learning models, we also applied the Grad-CAM method to further understand and interpret the model's predictions. © 2022, Sakarya University. All rights reserved.