A Deep Learning-based Convolutional Neural Networks Model for White Blood Cell Classification

Abstract

White blood cells, or leukocytes, are indispensable for the optimal functioning of the immune system. They play a critical role in protecting the body against infections, diseases, and other foreign invaders by identifying and fighting harmful bacteria and pathogens that can cause illness. Additionally, they contribute to the elimination of dead and damaged cells from the body and facilitate tissue healing and repair processes. The absence of white blood cells would render the body defenceless against infections and diseases, exposing it to a variety of harmful pathogens. This could result in significant health issues and potentially even lead to death in severe instances. White blood cell classification is an important task in medical diagnosis and treatment because healthcare professionals diagnose and treat a variety of immune system-related diseases and conditions, including autoimmune disorders, infections, and cancers by identifying the structure, characteristics and functions of white blood cells. In this work, a convolutional neural network (CNN) model has been trained to classify white blood cells. The proposed model has achieved an accuracy of 88.78%, which has been identified as the highest among all the models implemented by various authors in the literature review. This implies that the proposed model has correctly classified white blood cells in almost 9 out of 10 cases. Moreover, the error rate of the model is only 0.108967 which indicates that the model is very reliable and consistent in its predictions. Additionally, this work shows the promising result for white blood cell classification using deep learning techniques. Furthermore, with improvements and refinements in the future, it can be possible to achieve higher levels of accuracy and precision, which could have a significant impact on medical diagnosis and treatment.