{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T12:12:46Z","timestamp":1788351166823,"version":"build-2803163510"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>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\u2013AUC of 0.979\u00b10.015, while the Swin-embedding XGBoost and hybrid Swin + GLCM\/LBP + XGBoost models achieved mean ROC\u2013AUC values of 0.977\u00b10.016 and 0.977\u00b10.017, respectively. The GLCM\/LBP-only model achieved a mean ROC\u2013AUC of 0.934\u00b10.041, 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.<\/jats:p>","DOI":"10.3390\/bioengineering13091024","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T10:57:23Z","timestamp":1788346643000},"page":"1024","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2008-0153","authenticated-orcid":false,"given":"Nokulunga","family":"Nhlapho","sequence":"first","affiliation":[{"name":"Center for Artificial Intelligence and Multidisciplinary Innovations, Department of Auditing, College of Accounting Sciences, University of South Africa, Pretoria 0002, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"George","family":"Obaido","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence and Multidisciplinary Innovations, Department of Auditing, College of Accounting Sciences, University of South Africa, Pretoria 0002, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5328-1497","authenticated-orcid":false,"given":"Ebenezer","family":"Esenogho","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence and Multidisciplinary Innovations, Department of Auditing, College of Accounting Sciences, University of South Africa, Pretoria 0002, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rathnaiah, G., Zinniel, D.K., Bannantine, J.P., Stabel, J.R., Gr\u00f6hn, Y.T., Collins, M.T., and Barletta, R.G. 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