{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:01:15Z","timestamp":1790683275731,"version":"4.1.0"},"reference-count":37,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ASELSAN Inc."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Cardiomegaly\u2014defined as the abnormal enlargement of the heart\u2014is a key radiological indicator of various cardiovascular conditions. Early detection is vital for initiating timely clinical intervention and improving patient outcomes. This study investigates the application of deep learning techniques for the automated diagnosis of cardiomegaly from chest X-ray (CXR) images, utilizing both convolutional neural networks (CNNs) and Vision Transformers (ViTs). Methods: We assembled one of the largest and most diverse CXR datasets to date, combining posteroanterior (PA) images from PadChest, NIH CXR, VinDr-CXR, and CheXpert. Multiple pre-trained CNN architectures (VGG16, ResNet50, InceptionV3, DenseNet121, DenseNet201, and AlexNet), as well as Vision Transformer models, were trained and compared. In addition, we introduced a novel stacking-based ensemble model\u2014Combined Ensemble Learning Model (CELM)\u2014that integrates complementary CNN features via a meta-classifier. Results: The CELM achieved the highest diagnostic performance, with a test accuracy of 92%, precision of 99%, recall of 89%, F1-score of 0.94, specificity of 92.0%, and AUC of 0.90. These results highlight the model\u2019s high agreement with expert annotations and its potential for reliable clinical use. Notably, Vision Transformers offered competitive performance, suggesting their value as complementary tools alongside CNNs. Conclusions: With further validation, the proposed CELM framework may serve as an efficient and scalable decision-support tool for cardiomegaly screening, particularly in resource-limited settings such as intensive care units (ICUs) and emergency departments (EDs), where rapid and accurate diagnosis is imperative.<\/jats:p>","DOI":"10.3390\/diagnostics15131602","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T03:53:02Z","timestamp":1750823582000},"page":"1602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0764-098X","authenticated-orcid":false,"given":"Erdem","family":"Yanar","sequence":"first","affiliation":[{"name":"Department of Healthcare Systems System Engineering, ASELSAN, 06200 Ankara, Turkey"},{"name":"Department of Electrical and Electronics Engineering, Gazi University, 06570 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1358-0756","authenticated-orcid":false,"given":"F\u0131rat","family":"Hardala\u00e7","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Gazi University, 06570 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9406-4694","authenticated-orcid":false,"given":"Kubilay","family":"Ayturan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Gazi University, 06570 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1016\/j.jacc.2022.11.001","article-title":"Global Burden of Cardiovascular Diseases and Risks Collaboration, 1990\u20132021","volume":"80","author":"Lindstrom","year":"2022","journal-title":"J. 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