{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T14:09:28Z","timestamp":1762265368479,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Northern Border University, Arar, KSA","award":["NBU-FFR-2025-750 159-04"],"award-info":[{"award-number":["NBU-FFR-2025-750 159-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Pneumonia remains a serious global health issue, particularly affecting vulnerable groups such as children and the elderly, where timely and accurate diagnosis is critical for effective treatment. Recent advances in deep learning have significantly enhanced pneumonia detection using chest X-rays, yet many current methods still face challenges with interpretability, efficiency, and clinical applicability. In this work, we proposed a YOLOv11-based deep learning framework designed for real-time pneumonia detection, strengthened by the integration of Grad-CAM for visual interpretability. To further enhance robustness, the framework incorporated preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, region-of-interest extraction, and lung segmentation, ensuring both precise localization and improved focus on clinically relevant features. Evaluation on two publicly available datasets confirmed the effectiveness of the approach. On the COVID-19 Radiography Dataset, the system reached a macro-average accuracy of 98.50%, precision of 98.60%, recall of 97.40%, and F1-score of 97.99%. On the Chest X-ray COVID-19 &amp; Pneumonia dataset, it achieved 98.06% accuracy, with corresponding high precision and recall, yielding an F1-score of 98.06%. The Grad-CAM visualizations consistently highlighted pathologically relevant lung regions, providing radiologists with interpretable and trustworthy predictions. Comparative analysis with other recent approaches demonstrated the superiority of the proposed method in both diagnostic accuracy and transparency. With its combination of real-time processing, strong predictive capability, and explainable outputs, the framework represents a reliable and clinically applicable tool for supporting pneumonia and COVID-19 diagnosis in diverse healthcare settings.<\/jats:p>","DOI":"10.3390\/a18110703","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T14:02:09Z","timestamp":1762264929000},"page":"703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Explainable YOLO-Based Deep Learning Framework for Pneumonia Detection from Chest X-Ray Images"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2535-3657","authenticated-orcid":false,"given":"Ali","family":"Ahmed","sequence":"first","affiliation":[{"name":"Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebin El-Kom 32511, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7242-5281","authenticated-orcid":false,"given":"Ali I.","family":"Siam","sequence":"additional","affiliation":[{"name":"College of Arts and Science, Umm Al Quwain University, Umm Al Quwain 536, United Arab Emirates"},{"name":"Department of Embedded Network Systems Technology, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4419-0737","authenticated-orcid":false,"given":"Ahmed E. Mansour","family":"Atwa","sequence":"additional","affiliation":[{"name":"Electronics and Communication Department, College of Engineering and Computer Science, Mustaqbal University, Buraydh 51411, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2149-4271","authenticated-orcid":false,"given":"Mohamed Ahmed","family":"Atwa","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Kasr Al-Ainy, Cairo University, Giza 12613, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5054-3703","authenticated-orcid":false,"given":"Elsaid Md.","family":"Abdelrahim","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Science, Northern Border University, Arar 91431, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4728-590X","authenticated-orcid":false,"given":"El-Sayed","family":"Atlam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia"},{"name":"Computer Science Department, Faculty of Science, University of Tanta, Tanta 31527, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"ref_1","unstructured":"(2025, May 01). 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