Efficient CNN-based classification of white blood cells: a comparative study of model performance

Abstract

White blood cells (WBCs) are integral components of the immune system, playing a pivotal role in defending the body against various diseases and infections. Accurate quantitative and qualitative analysis of different WBC types is essential for the effective diagnosis and treatment of numerous medical conditions. Counting and classifying these cells are fundamental steps in blood sample examination and testing. This study focuses on evaluating three convolutional neural network (CNN)-based architectures – ResNet-50, VGG-16, and a classic CNN – for classifying blood cell image categories using a curated dataset from Kaggle. We trained and evaluated each model’s performance using accuracy curves, confusion matrices, and classification reports. Among the tested architectures, the ResNet-50 model achieved the highest validation accuracy of approximately 80%, followed by VGG-16 at 79.6% and the classic CNN at 76.2%. Both VGG-16 and the classic CNN exhibited significant overfitting, with large gaps between training and validation accuracy. These findings highlight the challenges of image classification on imbalanced datasets and suggest directions for future improvements through data augmentation and architectural refinements.