{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:29:02Z","timestamp":1791912542326,"version":"4.3.4"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002380","name":"Hanyang University","doi-asserted-by":"crossref","award":["HY- 2024-2692"],"award-info":[{"award-number":["HY- 2024-2692"]}],"id":[{"id":"10.13039\/501100002380","id-type":"DOI","asserted-by":"crossref"}]},{"name":"MSIT (Ministry of Science and ICT), Korea","award":["IITP-2024-RS-202400423071"],"award-info":[{"award-number":["IITP-2024-RS-202400423071"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In the case of neonates, especially low birth weight preterm and high-risk infants, portable X-rays are frequently used. However, the image quality of portable X-rays is significantly lower compared to standard adult or pediatric X-rays, leading to considerable challenges in identifying abnormalities. Although attempts have been made to introduce deep learning to address these image quality issues, the poor quality of the images themselves hinders the training of deep learning models, further emphasizing the need for image enhancement. Additionally, since neonates have a high cell division rate and are highly sensitive to radiation, increasing radiation exposure to improve image quality is not a viable solution. Therefore, it is crucial to enhance image quality through preprocessing before training deep learning models. While various image enhancement methods have been proposed, Contrast Limited Adaptive Histogram Equalization (CLAHE) has been recognized as an effective technique for contrast-based image improvement. However, despite extensive research, the process of setting CLAHE\u2019s hyperparameters still relies on a brute force, manual approach, making it inefficient. To address this issue, we propose a method called Bayesian Optimization CLAHE(BO-CLAHE), which leverages Bayesian optimization to automatically select the optimal hyperparameters for X-ray images used in diagnosing lung diseases in preterm and high-risk neonates. The images enhanced by BO-CLAHE demonstrated superior performance across several classification models, with particularly notable improvements in diagnosing Transient Tachypnea of the Newborn (TTN). This approach not only reduces radiation exposure but also contributes to the development of AI-based diagnostic tools, playing a crucial role in the early diagnosis and treatment of preterm and high-risk neonates.<\/jats:p>","DOI":"10.1038\/s41598-025-88451-0","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T05:14:22Z","timestamp":1739164462000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["BO-CLAHE enhancing neonatal chest X-ray image quality for improved lesion classification"],"prefix":"10.1038","volume":"15","author":[{"given":"Jiwon","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byungmin","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Young","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeonjoon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"88451_CR1","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1109\/TETCI.2023.3320971","volume":"8","author":"B Subramani","year":"2024","unstructured":"Subramani, B., Veluchamy, M. & Bhandari, A. K. Optimal fuzzy intensification system for contrast distorted medical images. IEEE Trans. Emerg. Top. Comput. Intell. 8, 992\u20131002. https:\/\/doi.org\/10.1109\/TETCI.2023.3320971 (2024).","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"88451_CR2","doi-asserted-by":"publisher","unstructured":"Sanchez Jacob, R. et al. Optimising the use of computed radiography in pediatric chest imaging. J. Digit. Imaging 22, 104\u2013113. https:\/\/doi.org\/10.1007\/s10278-007-9071-2 (2009).","DOI":"10.1007\/s10278-007-9071-2"},{"key":"88451_CR3","doi-asserted-by":"publisher","unstructured":"Mei, J., Jia, F., Chen, W. & Li, B. DEEPS: A novel framework for image quality improvement of x-ray non-destructive testing. In Proceedings of the 2023 8th International Conference on Multimedia and Image Processing, ICMIP \u201923. https:\/\/doi.org\/10.1145\/3599589.3599592 (Association for Computing Machinery, 2023).","DOI":"10.1145\/3599589.3599592"},{"key":"88451_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12903-021-01777-9","volume":"21","author":"L Kats","year":"2021","unstructured":"Kats, L., Goldman, Y. & Kahn, A. Automatic detection of image sharpening in maxillofacial radiology. BMC Oral Health 21, 1\u20138. https:\/\/doi.org\/10.1186\/s12903-021-01777-9 (2021).","journal-title":"BMC Oral Health"},{"key":"88451_CR5","unstructured":"Rennie, J.\u00a0M. Roberton\u2019s Textbook of Neonatology."},{"key":"88451_CR6","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1148\/radiol.2261011924","volume":"226","author":"PK Shah","year":"2003","unstructured":"Shah, P. K. et al. Missed non-small cell lung cancer: Radiographic findings of potentially resectable lesions evident only in retrospect. Radiology 226, 235\u2013241. https:\/\/doi.org\/10.1148\/radiol.2261011924 (2003).","journal-title":"Radiology"},{"key":"88451_CR7","unstructured":"danielnflam. Deep Learning Models for Bone Suppression in Chest Radiographs (2023). Original publication date 30 Apr 2021."},{"key":"88451_CR8","doi-asserted-by":"publisher","first-page":"113","DOI":"10.14257\/ijseia.2013.7.5.11","volume":"7","author":"BS Min","year":"2013","unstructured":"Min, B. S., Lim, D. K., Kim, S. J. & Lee, J. H. A novel method of determining parameters of Clahe based on image entropy. IJSEIA 7, 113\u2013120 (2013).","journal-title":"IJSEIA"},{"key":"88451_CR9","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s13640-019-0445-4","volume":"2019","author":"GFC Campos","year":"2019","unstructured":"Campos, G. F. C. et al. Machine learning hyperparameter selection for contrast limited adaptive histogram equalization. J. Image Video Proc. 2019, 59. https:\/\/doi.org\/10.1186\/s13640-019-0445-4 (2019).","journal-title":"J. Image Video Proc."},{"key":"88451_CR10","doi-asserted-by":"publisher","first-page":"211301","DOI":"10.1007\/s11432-024-4133-3","volume":"67","author":"X Min","year":"2024","unstructured":"Min, X., Duan, H., Sun, W., Zhu, Y. & Zhai, G. Perceptual video quality assessment: A survey. Sci. China Inf. Sci. 67, 211301. https:\/\/doi.org\/10.1007\/s11432-024-4133-3 (2024).","journal-title":"Sci. China Inf. Sci."},{"key":"88451_CR11","unstructured":"Ovcharenko, I. IlliaOvcharenko\/Lung-Segmentation (2024). Original-date: 2019-06-01T11:19:11Z."},{"key":"88451_CR12","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1080\/1206212X.2021.1974663","volume":"44","author":"DM Belete","year":"2022","unstructured":"Belete, D. M. & Huchaiah, M. D. 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