{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T05:58:49Z","timestamp":1774677529136,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute of Information and Communications Technology Planning and Evaluation (IITP)\u2014Innovative Human Resource Development for Local Intellectualization program","doi-asserted-by":"publisher","award":["IITP-2025-RS-2024-00436773"],"award-info":[{"award-number":["IITP-2025-RS-2024-00436773"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Early detection of tuberculosis plays a critical role in effective treatment management. Like active tuberculosis, early identification of inactive forms such as latent or healed tuberculosis is essential to prevent future reactivation. In this study, we developed a deep-learning-based binary classification model to distinguish between active and inactive tuberculosis cases. Our model architecture incorporated an EfficientNet backbone with an MLP-Mixer classification head and was fine-tuned on a dataset annotated by Cheonan Soonchunhyang Hospital. To enhance predictive performance, we applied transfer learning using weights pre-trained on the JFT-300M dataset via the Noisy Student training method. Unlike conventional models, our approach achieved competitive results, with an accuracy of 96.3%, a sensitivity of 95.9%, and a specificity of 96.6% on the test set. These promising outcomes suggest that our model could serve as a valuable asset to support clinical decision-making and streamline early screening workflows for latent tuberculosis.<\/jats:p>","DOI":"10.3390\/bioengineering12060630","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T05:11:49Z","timestamp":1749532309000},"page":"630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Active and Inactive Tuberculosis Classification Using Convolutional Neural Networks with MLP-Mixer"],"prefix":"10.3390","volume":"12","author":[{"given":"Beanbonyka","family":"Rim","sequence":"first","affiliation":[{"name":"Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3517-6175","authenticated-orcid":false,"given":"Hyeonung","family":"Jang","sequence":"additional","affiliation":[{"name":"Haewootech Co., Ltd., Busan 46742, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongchang","family":"Lee","sequence":"additional","affiliation":[{"name":"Haewootech Co., Ltd., Busan 46742, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8887-2513","authenticated-orcid":false,"given":"Wangsu","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department Computer Engineering, Kyungnam University, Changwon 51767, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2016). 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