{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:03:59Z","timestamp":1791914639618,"version":"4.3.4"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research","award":["1587-611-2025"],"award-info":[{"award-number":["1587-611-2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Healthcare systems worldwide face increasing pressure to deliver accurate, affordable, and scalable diagnostic services while maintaining long-term sustainability. Chest X-ray screening is considered one of the most cost-effective methods for detecting lung disease. However, many deep learning approaches are computationally intensive and difficult to interpret, which limits their adoption in high-throughput, resource-constrained clinical settings. This study proposes a hybrid CNN\u2013tree framework for automated lung disease classification from chest X-ray images, which targets COVID-19, pneumonia, tuberculosis, lung cancer, and normal cases. To ensure robustness and generalization, four publicly available chest X-ray datasets from different sources are merged into a unified five-class dataset, which introduces realistic variations in imaging conditions and patient populations. A ResNet-18 model is fine-tuned to extract domain-specific deep feature representations. Feature dimensionality and redundancy are reduced using Principal Component Analysis, while class imbalance is addressed through the Synthetic Minority Over-sampling Technique. The resulting compact feature vectors are used to train interpretable tree-based classifiers, which include Decision Tree, Random Forest, and XGBoost. Experiments conducted using five-fold stratified cross-validation demonstrate substantial and consistent performance gains. When trained on fine-tuned and preprocessed deep features, all evaluated tree-based classifiers achieve weighted F1-scores between 0.977 and 0.982 using five-fold cross-validation, with a significant reduction in inter-class confusion. In addition, the proposed framework maintains low per-sample inference latency, which supports energy-efficient and scalable deployment. These results indicate that combining deep feature learning with interpretable tree-based models provides a practical and reliable solution for sustainable chest X-ray screening in real-world clinical environments.<\/jats:p>","DOI":"10.3390\/bioengineering13030267","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T10:33:02Z","timestamp":1772101982000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving Tree-Based Lung Disease Classification from Chest X-Ray Images Using Deep Feature Representations"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2931-8744","authenticated-orcid":false,"given":"Abdulaziz A.","family":"Alsulami","sequence":"first","affiliation":[{"name":"Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2422-0297","authenticated-orcid":false,"given":"Qasem","family":"Abu Al-Haija","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity, Faculty of Computer & Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rayed","family":"Alakhtar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huda","family":"Alsobhi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, Taif University, Taif 21974, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5845-7183","authenticated-orcid":false,"given":"Rayan A.","family":"Alsemmeari","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6913-1525","authenticated-orcid":false,"given":"Badraddin","family":"Alturki","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4900-729X","authenticated-orcid":false,"given":"Ahmad J.","family":"Tayeb","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nakrani, H., Shahra, E.Q., Basurra, S., Mohammad, R., Vakaj, E., and Jabbar, W.A. 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