Automated Classification of Leukocytes: Deep Learning Model Analysis Based on Granule Content
Kilic, Rabiye, Saglam, Hilal Kubra, Oral, Emin Argun, Ozbek, Ibrahim Yigit
Automatic classification of white blood cells according to their granule content is of critical importance for hematological analysis and disease diagnosis. In this study, the performances of DarkNet-19 and DarkNet-53 models were evaluated for deep learning-based classification. First, two-class classification was performed for the separation of Granulocytes and AnGranulocytes, and the DarkNet-19 model achieved 94.8% and DarkNet-53 model 98.0% accuracy. Then, each cell type was analyzed separately with six-class classification, and DarkNet-19 achieved 91.37% and DarkNet-53 achieved 96.53% accuracy. In particular, the DarkNet-53 model showed a significant improvement by providing higher F1-scores in cell types with low samples. The findings show that the DarkNet-53 model can distinguish cell morphology more successfully thanks to its deeper architecture and is a strong candidate for integration into automatic leukocyte classification systems.
- Dergi
- 2025 33RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU
- Yıl
- 2025
- ISSN
- 2165-0608
- DOI
- 10.1109/SIU66497.2025.11111779
- WoS UID
- WOS:001575462500020