A Comparative Analysis for Leukocyte Classification Based on Various Deep Learning Models Using Transfer Learning

Abstract

Leukocytes, sometimes referred to as white blood cells (WBCs), are crucial to the healthy operation of the human body. WBC distribution in human body are biological markers that determine the immunity of human body to fight against infectious diseases. WBC detection and classification plays an important role in medical application. However, using manual microscopic evaluation is complicated and time consuming. To tackle the limitations associated with traditional methods, recently deep learning (D.L) based methods are much experimented and explored. In this paper, we implemented various D.L models for automatic classification of WBCs. A comparative study among pretrained networks namely Inceptionv3, MobileNetV3 and VGG-19 was performed using transfer learning on publicly available WBC images from Kaggle. Classification accuracy of WBC images using Inceptionv3, MobileNetV3 and VGG-19 is 99.76%, 99.25% and 86.50% respectively. Inceptionv3 was further compared with the existing works in the literature and is found to be superior.