White blood cell classification is fundamental to hematological diagnosis, yet existing datasets are limited in scale and class diversity. We present a comprehensive peripheral blood cell dataset comprising 31,489 high-resolution microscopic images across 13 distinct cell classes, representing the largest publicly available collection for automated blood cell analysis. Images are acquired using the Sysmex DI-60 system from May-Grünwald-Giemsa-stained blood smears at 100 × magnification under standardized laboratory conditions. Expert hematologists with over 10 years of experience performed manual annotation with high inter-rater agreement (Cohen's kappa >0.85 for all classes). The dataset includes common cell types such as segmented neutrophils and lymphocytes, alongside diagnostically critical but rare subtypes, including myelocytes, blasts, and reactive lymphocytes. Images are organized into training, validation, and test splits (70:10:20 ratio) with consistent 368 × 368 pixel resolution. Baseline experiments using 14 deep learning architectures demonstrate the dataset's utility, with DenseNet-121 achieving 95.23% accuracy. KU-Optofil PBC Dataset addresses critical gaps in medical image analysis datasets and supports the development of robust automated hematology systems for clinical applications.
Yarıkan et al. (Fri,) studied this question.