{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:21:12Z","timestamp":1774369272445,"version":"3.50.1"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Early and accurate diagnosis of pneumonia is crucial to improve cure rates and reduce mortality. Traditional chest X-ray analysis relies on physician experience, which can lead to subjectivity and misdiagnosis. To address this, we propose a novel pneumonia diagnosis method using the Fast-YOLO deep learning network that we introduced. First, we constructed a pneumonia dataset containing five categories and applied image enhancement techniques to increase data diversity and improve the model\u2019s generalization ability. Next, the YOLOv11 network structure was redesigned to accommodate the complex features of pneumonia X-ray images. By integrating the C3k2 module, DCNv2, and DynamicConv, the Fast-YOLO network effectively enhanced feature representation and reduced computational complexity (FPS increased from 53 to 120). Experimental results subsequently show that our method outperforms other commonly used detection models in terms of accuracy, recall, and mAP, offering better real-time detection capability and clinical application potential.<\/jats:p>","DOI":"10.3389\/fnbot.2025.1576438","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T05:30:19Z","timestamp":1745818219000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Diagnosis of pneumonia from chest X-ray images using YOLO deep learning"],"prefix":"10.3389","volume":"19","author":[{"given":"Yanchun","family":"Xie","sequence":"first","affiliation":[]},{"given":"Binbin","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Hailong","family":"Yu","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"34691","DOI":"10.1109\/ACCESS.2024.3372588","article-title":"Pneumonia detection using chest radiographs with novel EfficientNetV2L model","volume":"12","author":"Ali","year":"2024","journal-title":"IEEE Access"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"6750","DOI":"10.3390\/s24206750","article-title":"Concatenated CNN-based pneumonia detection using a fuzzy-enhanced dataset","volume":"24","author":"Buriboev","year":"2024","journal-title":"Sensors"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.18502\/ijm.v15i1.11914","article-title":"Evaluation PCR panel of the FilmArray\u00ae pneumonia plus for pathogen detection of ventilator-associated pneumonia in children and its impact on therapeutic management","volume":"15","author":"Debbagh","year":"2023","journal-title":"Iran. J. Microbiol."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.patrec.2020.12.010","article-title":"Customized VGG19 architecture for pneumonia detection in chest X-rays","volume":"143","author":"Dey","year":"2021","journal-title":"Pattern Recogn. Lett."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-18975-6","article-title":"Pneumonia detection in chest x-ray images using an optimized ensemble with XGBoost classifier","author":"El-Ghandour","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13369-024-09896-5","article-title":"Transparency in diagnosis: unveiling the power of deep learning and explainable AI for medical image interpretation","author":"Garg","year":"2025","journal-title":"Arab. J. Sci. Eng."},{"key":"ref7","first-page":"10","article-title":"Performance convolutional neural network (CNN) and support vector machine (SVM) on tuberculosis disease classification based on X-ray image","volume":"2025","author":"Hakim","year":"2025","journal-title":"Commun. Math. Biol. Neurosci."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"4931","DOI":"10.1038\/s41598-025-88451-0","article-title":"BO-CLAHE enhancing neonatal chest X-ray image quality for improved lesion classification","volume":"15","author":"Han","year":"2025","journal-title":"Sci. Rep."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"15503","DOI":"10.1007\/s00521-023-08566-1","article-title":"A classical\u2013quantum convolutional neural network for detecting pneumonia from chest radiographs","volume":"35","author":"Kulkarni","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1615\/IntJMultCompEng.2024053584","article-title":"Secure and efficient classification of chest X-ray images using cryptography and machine learning techniques","volume":"23","author":"Lekshmy","year":"2025","journal-title":"Int. J. Multiscale Comput. Eng."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1506735","DOI":"10.3389\/fped.2025.1506735","article-title":"Clinical characteristics of Kawasaki disease with pulmonary radiographic abnormalities and its impact on the incidence of coronary artery lesions: a randomized retrospective cohort study","volume":"13","author":"Liu","year":"2025","journal-title":"Front. Pediatr."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"106981","DOI":"10.1016\/j.bspc.2024.106981","article-title":"CTBViT: a novel ViT for tuberculosis classification with efficient block and randomized classifier","volume":"100","author":"Lu","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2212.07784","article-title":"Rtmdet: an empirical study of designing real-time object detectors","author":"Lyu","year":"2022","journal-title":"Arxiv"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"6448","DOI":"10.3390\/app12136448","article-title":"Pneumonia detection on chest X-ray images using Ensemble of Deep Convolutional Neural Networks","volume":"12","author":"Mabrouk","year":"2022","journal-title":"Appl. Sci."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"5952","DOI":"10.1109\/TCE.2024.3413893","article-title":"Pneumonia detection using asynchronous Split learning method","volume":"70","author":"Majumder","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12890-025-03551-x","article-title":"Exploring the role of respiratory virus infections in aspiration pneumonia: a comprehensive analysis of cases with lower respiratory tract infections[J]","volume":"25","author":"Nabeya","year":"2025","journal-title":"BMC Pulm. Med."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2410.13842","article-title":"D-FINE: redefine regression task in DETRs as fine-grained distribution refinement","author":"Peng","year":"2024","journal-title":"Arxiv"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"109464","DOI":"10.1016\/j.asoc.2022.109464","article-title":"An adaptive and altruistic PSO-based deep feature selection method for pneumonia detection from chest X-rays","volume":"128","author":"Pramanik","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"109659","DOI":"10.1016\/j.compbiomed.2025.109659","article-title":"Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network","volume":"186","author":"Prince","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"112258","DOI":"10.1016\/j.asoc.2024.112258","article-title":"An explainable contrastive-based dilated convolutional network with transformer for pediatric pneumonia detection","volume":"167","author":"Raghaw","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"107100","DOI":"10.1016\/j.bspc.2024.107100","article-title":"An optimized Wasserstein deep convolutional generative adversarial network approach for the classification of COVID-19 and pneumonia","volume":"100","author":"Rajendra","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"119854","DOI":"10.1016\/j.ins.2023.119854","article-title":"CheXMed: a multimodal learning algorithm for pneumonia detection in the elderly","volume":"654","author":"Ren","year":"2024","journal-title":"Inf. Sci."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"24101","DOI":"10.1007\/s11042-023-16419-1","article-title":"A systematic literature review on deep learning approaches for pneumonia detection using chest X-ray images","volume":"83","author":"Sharma","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"112708","DOI":"10.1016\/j.knosys.2024.112708","article-title":"TGPO-WRHNN: two-stage grad-CAM-guided PMRS optimization and weighted-residual hypergraph neural network for pneumonia detection","volume":"306","author":"Tang","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1109\/TCBB.2023.3247483","article-title":"A deep learning method for pneumonia detection based on fuzzy non-maximum suppression","volume":"21","author":"Wu","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"125355","DOI":"10.1016\/j.eswa.2024.125355","article-title":"BSD: a multi-task framework for pulmonary disease classification using deep learning","volume":"259","author":"Yi","year":"2025","journal-title":"Expert Syst. Appl."}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2025.1576438\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T22:21:48Z","timestamp":1746138108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2025.1576438\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,28]]},"references-count":26,"alternative-id":["10.3389\/fnbot.2025.1576438"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2025.1576438","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,28]]},"article-number":"1576438"}}