{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T09:11:36Z","timestamp":1768036296038,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Graduate Studies and Scientific Research at Jouf University","award":["DGSSR-2025-FC-01067"],"award-info":[{"award-number":["DGSSR-2025-FC-01067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Tuberculosis (TB) is a significant global health issue, particularly in resource-limited regions where radiological expertise is constrained. This project aims to develop a scalable deep learning system that safeguards privacy and achieves high accuracy in the early identification of tuberculosis using chest X-ray images. The objective is to implement federated learning with an adaptive-weighted ensemble optimised by a Genetic Algorithm (GA) to address the challenges of centralised training and single-model approaches. Method: We developed an ensemble learning method that combines multiple locally trained models to improve diagnostic consistency and reduce individual-model bias. An optimisation system that autonomously selected the optimal ensemble weights determined each model\u2019s contribution to the final decision. A controlled augmentation process was employed to enhance the model\u2019s robustness and reduce the likelihood of overfitting by introducing realistic alterations to appearance, geometry, and acquisition conditions. Federated learning facilitated collaboration among universities for training while ensuring data privacy was maintained during the establishment of the optimal ensemble at each location. In this system, just model parameters were transmitted, excluding patient photographs. This enabled the secure amalgamation of global data without revealing sensitive clinical information. Standard diagnostic metrics, including accuracy, sensitivity, precision, F1 score, AUC, and confusion matrices, were employed to evaluate the model\u2019s performance. Results: The proposed federated, GA-optimized ensemble demonstrated superior performance compared with individual models and fixed-weight ensembles. The system achieved 98% accuracy, 97% F1 score, and 0.999 AUC, indicating highly reliable discrimination between TB-positive and typical cases. Federated learning preserved model robustness across heterogeneous data sources, while ensuring complete patient privacy. Conclusions: The proposed federated, GA-optimized ensemble achieves highly accurate and robust early tuberculosis detection while preserving patient privacy across distributed clinical sites. This scalable framework demonstrates strong potential for reliable AI-assisted TB screening in resource-limited healthcare settings.<\/jats:p>","DOI":"10.3390\/diagnostics16020204","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T14:32:38Z","timestamp":1767882758000},"page":"204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early Tuberculosis Detection via Privacy-Preserving, Adaptive-Weighted Deep Models"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0138-2226","authenticated-orcid":false,"given":"Karim","family":"Gasmi","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2246-5656","authenticated-orcid":false,"given":"Afrah","family":"Alanazi","sequence":"additional","affiliation":[{"name":"Department of Information System, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9444-8209","authenticated-orcid":false,"given":"Najib","family":"Ben Aoun","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information, Al-Baha University, Alaqiq 65779-7738, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2200-0194","authenticated-orcid":false,"given":"Mohamed O.","family":"Altaieb","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6325-9069","authenticated-orcid":false,"given":"Alameen E. M.","family":"Abdalrahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omer","family":"Hamid","sequence":"additional","affiliation":[{"name":"Cybersecurity Department, College of Engineering and Information Technology, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1423-5588","authenticated-orcid":false,"given":"Sahar","family":"Almenwer","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4698-3693","authenticated-orcid":false,"given":"Lassaad","family":"Ben Ammar","sequence":"additional","affiliation":[{"name":"College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4041-836X","authenticated-orcid":false,"given":"Samia","family":"Yahyaoui","sequence":"additional","affiliation":[{"name":"Department of Physics, College of Science, Jouf University, Sakaka 72341, Aljouf, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7638-9939","authenticated-orcid":false,"given":"Manel","family":"Mrabet","sequence":"additional","affiliation":[{"name":"College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1007\/s15010-024-02215-y","article-title":"Incidence and risk factors for HIV-tuberculosis coinfection in the Cologne\u2013Bonn region: A retrospective cohort study","volume":"52","author":"Rauschning","year":"2024","journal-title":"Infection"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1111\/tmi.13509","article-title":"Outcomes and complications of hospitalised patients with HIV-TB co-infection","volume":"26","author":"Agudelo","year":"2021","journal-title":"Trop. 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