Work

Interpretable deep learning-based classification of polycythemia vera, secondary erythrocytosis, and healthy controls via tabular-to-color image transformation of routine blood parameters

Year: 2026

Type: article

Abstract: Polycythemia Vera (PV) and Secondary Erythrocytosis (SE) are hematological conditions with overlapping clinical features, particularly elevated hemoglobin and hematocrit levels, which complicate differential diagnosis in routine practice. This study proposes a novel tabular-to-image deep learning framework for the classification of PV, SE, and Healthy Controls (HC) using routine hematological data. A total of 345 clinical samples (111 PV, 143 SE, and 91 HC) described by 28 hematological variables were processed through a multi-stage pipeline including MICE-based missing value imputation, feature engineering, mRMR feature selection, and deterministic HSV-to-RGB encoding of 36 selected features into 6 × 6 color-mapped grid images. Six state-of-the-art architectures (DINOv2, EfficientNetV2, ConvNeXt, Swin Transformer, ResNeXt-50, and Vision Transformer) were fine-tuned using transfer learning and Bayesian hyperparameter optimization with Optuna. Performance was evaluated using stratified 10-fold cross-validation, and model interpretability was examined with Grad-CAM and SHAP analyses. ResNeXt-50 achieved the best performance, with a macro F1-score of 0.9250 ± 0.0356 and an accuracy of 92.3%, while all models exceeded 91% accuracy. SHAP and Grad-CAM findings indicated that the models relied on clinically meaningful hematological features. These results demonstrate that color-mapped image representations of routine blood parameters provide an effective and interpretable basis for automated erythrocytosis classification, with potential utility as a computer-aided diagnostic tool in clinical hematology.

Source: Scientific Reports

DOI: 10.1038/s41598-026-69846-z

Authors: Sinan Demircioğlu, Mesut Ersin Sönmez, Gürkan Yarbaş, Muhsin Yılmaz, Filiz Alkan Baylan

Institutions: Necmettin Erbakan University, Karamanoğlu Mehmetbey University

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690