WBC Subtype Detection using Deep Learning with Optimizing Hyperparameters by Genetic Algorithm

The human body's defense mechanism, the immune system, heavily relies on white blood cells (WBCs). These cells come in five subtypes: Eosinophil, Basophil, Neutrophils, Monocyte, and Lymphocyte. Each subtype plays a vital role in the diagnosis of hematological diseases, such as leukemia, as their presence or absence can indicate the presence of a disease. In the past, hematologists have manually examined blood smears under a microscope to detect the subtype of WBCs. However, this method is both time-consuming and prone to errors. This study proposes a new approach, which uses a deep learning-based convolutional neural network (CNN) architecture in combination with an evolution-based algorithm (genetic algorithm) to detect WBC subtypes with greater accuracy. A large dataset of 12,000 images was used for training and analysis. The proposed model, which utilizes the You Only Look Once version 5 (YOLOv5) for image localization and segmentation, was able to accurately localize WBCs with an IoU score of 94% for more than 80% of images and detect the subtypes with an accuracy of 98%. This study aims to overcome the limitations of previous research by providing a more efficient and accurate method for detecting WBC subtypes.