Design and Investigate the Deep Learning Models for White Blood Cell Classification

For the purpose of illness prediction, the objective of this study is to develop automated models for the categorization of white blood cells (WBCs). In the human immune system, white blood cells (WBCs) function to combat infections and shield the body from external substances. Eosinophils, neutrophils, monocytes, basophils, and lymphocytes are the components that make up these cells; each of these cell types accounts for a different proportion and is responsible for a distinct set of functions overall. The clinical laboratory technique for counting the specific types of white blood cells (WBCs) has long been an essential component of a testing procedure known as a complete blood count (CBC), which is used to aid in the monitoring of individuals' health. However, such manual processes are time-consuming and error-prone for disease detection. The recently proposed machine and deep learning-based solutions for the blood cell classification delivered better results, however, suffered from challenges as well. These challenges motivate us to propose a novel AI-based approach for automatic WBCs classification in this research work.