A Hybrid Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis

Paratuberculosis is a chronic infectious disease associated with substantial economic losses in livestock production. Histopathological examination remains an important diagnostic approach but can be time-consuming and dependent on specialist expertise, motivating the development of automated and interpretable image-classification methods. This study evaluated an explainable framework integrating a pretrained Swin-Tiny Transformer, handcrafted Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) texture descriptors, and XGBoost classification for paratuberculosis histopathology image analysis. Following duplicate screening, 349 unique images comprising 199 MAP-positive and 150 MAP-negative samples were evaluated using stratified image-level five-fold cross-validation. Four model configurations were compared to assess the independent and incremental contributions of the learned and handcrafted feature representations. The standalone Swin-Tiny model achieved the highest mean ROC-AUC of 0.979±0.015, while the Swin-embedding XGBoost and hybrid Swin + GLCM/LBP + XGBoost models achieved mean ROC-AUC values of 0.977±0.016 and 0.977±0.017, respectively. The GLCM/LBP-only model achieved a mean ROC-AUC of 0.934±0.041, indicating that the handcrafted texture descriptors contained independently discriminative information but provided limited incremental value when combined with the Swin embeddings. Grad-CAM and XGBoost feature-importance analyses provided image-level and feature-level insights into model predictions. These findings demonstrate the effectiveness of Swin-Tiny representations for paratuberculosis histopathology image classification while highlighting the need for external validation using larger, independently sourced datasets.