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This study presents an optimized EfficientNetB0 framework designed explicitly for multi-label classification of chest X-rays using the NIH dataset, integrating CLAHE-based contrast enhancement, strategic class balancing, and a comparative transfer learning strategy that preserves the dataset\u2019s inherent multi-label complexity. The proposed model achieved superior diagnostic performance with a macro-average AUC of 0.906 and recall of 0.824, outperforming DenseNet121 and MobileNetV2, and demonstrated strong per-class discrimination, especially for Pneumonia (AUC\u2009=\u20090.950) and Cardiomegaly (AUC\u2009=\u20090.946). These results confirm that the framework effectively balances learning capacity and generalization in a realistic multi-label clinical setting, offering a robust, interpretable solution suitable for computer-aided diagnosis where accurate detection of co-occurring thoracic pathologies is critical.<\/jats:p>","DOI":"10.1038\/s41598-026-42492-1","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T10:54:39Z","timestamp":1774695279000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification"],"prefix":"10.1038","volume":"16","author":[{"given":"Nagwa Yaseen","family":"Hegazy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed S.","family":"Sawah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,28]]},"reference":[{"key":"42492_CR1","unstructured":"RadiologyInfo.org. 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