{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:34:43Z","timestamp":1759332883221,"version":"3.37.3"},"reference-count":88,"publisher":"Wiley","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020912","name":"King Faisal University","doi-asserted-by":"publisher","award":["5,740"],"award-info":[{"award-number":["5,740"]}],"id":[{"id":"10.13039\/501100020912","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2024,2,23]]},"abstract":"<jats:p>The potential of technology to revolutionize healthcare is exemplified by the synergy between artificial intelligence (AI) and early detection of cardiomegaly, demonstrating the power of proactive intervention in cardiovascular health. This paper presents an innovative approach that leverages advanced AI algorithms, specifically deep learning (DL) technology, for the early detection of cardiomegaly. The methodology consists of five key steps, including data collection, image preprocessing, data augmentation, feature extraction, and classification. Utilizing chest X-ray (CXR) images from the National Institutes of Health (NIH), the study applies rigorous image preprocessing operations, including color transformation and normalization. To enhance model generalization, data augmentation is employed, paving the way for two distinct DL models, a convolutional neural network (CNN) developed from scratch and a pretrained residual network with 50 layers (ResNet50), and adapted to the problem domain. Both models are systematically evaluated with five optimizers, revealing the AdaMax optimizer\u2019s superiority for the CNN model and AdaGrad\u2019s efficacy for the modified ResNet50. The proposed CNN with AdaMax achieves an impressive 99.91% accuracy, outperforming recent techniques in precision, recall, and <jats:inline-formula><a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><a:mi>F<\/a:mi><a:mn>1<\/a:mn><a:mo>\u2212<\/a:mo><a:mtext>score<\/a:mtext><\/a:math><\/jats:inline-formula>. This research underscores the transformative potential of AI in cardiovascular health diagnostics, emphasizing the significance of timely intervention.<\/jats:p>","DOI":"10.1155\/2024\/8997093","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T22:50:07Z","timestamp":1708728607000},"page":"1-38","source":"Crossref","is-referenced-by-count":10,"title":["A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-2705","authenticated-orcid":true,"given":"Shaymaa E.","family":"Sorour","sequence":"first","affiliation":[{"name":"Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia"},{"name":"Faculty of Specific Education, Kafrelsheikh University, Kafrelsheikh 33511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2229-476X","authenticated-orcid":true,"given":"Abeer A.","family":"Wafa","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Artificial Intelligence, Helwan University, Helwan, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7408-5073","authenticated-orcid":true,"given":"Amr A.","family":"Abohany","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2808-0623","authenticated-orcid":true,"given":"Reda M.","family":"Hussien","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2018.2806086"},{"first-page":"109","article-title":"Deep learning for grading cardiomegaly severity in chest x-rays: an investigation","author":"S. 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