Accurate differentiation of mononuclear cells is essential for diagnosing hematological conditions. Nonetheless, their similar morphology can lead to misclassification. The application of artificial intelligence enables the extraction of discriminative features from cell images through advanced mathematical modeling. While a compact model is necessary to enable dynamic-monitoring in clinical practice, achieving high accuracy typically requires large, well-balanced datasets, presenting significant challenges in balancing model complexity and classification performance. This study compares traditional features like Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Gray-Level Co-occurrence Matrix (GLCM) with transfer learning using fine-tuned Vision transformer (ViT) for capturing features of images. Using a dataset of 4,315 images focusing on mononuclear cells, we integrated class balancing techniques and tested models with ten classifiers. Results showed HOG with XGBoost achieved 0.77 accuracy, while fine-tuned ViT with augmentation and XGBoost reached 0.82. The computational intensity of ViT is offset by its real-time monitoring potential.
Chokchaipermpoonphol et al. (2026) studied this question.