{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T06:04:15Z","timestamp":1790057055232,"version":"4.0.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"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. Med."],"abstract":"<jats:p>Lung imaging enables direct visualization of lesions and is vital for pneumonia diagnosis. However, current deep learning models show unsatisfactory classification accuracy and generalization for multi-class lung disease recognition, limiting their clinical decision-support value. To tackle this issue, we present LPC-Transformer, a framework optimized via improved training strategies with Swin Transformer as the backbone; performance gains are realized without deepening the network. We propose an LMF hybrid loss integrating Focal Loss and LDAMLoss to mitigate class imbalance. Furthermore, the SCION optimizer is adopted to stabilize training, and PolyReLU is used in MLP modules to model complex nonlinear features. Evaluated on CL-COVIDset containing 11 classes of lung CT images, our method is compared with mainstream benchmark networks. Experiments reveal that LPC-Transformer surpasses the baseline SwinT model substantially, with accuracy, recall, precision and F1-score improved by 4.2%, 4.4%, 2.0% and 3.8%, respectively. These results validate the effectiveness of the proposed training strategy optimization.<\/jats:p>","DOI":"10.3389\/fmed.2026.1960913","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:37:30Z","timestamp":1790055450000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LPC-transformer: a training strategy optimized framework for multiclass pneumonia medical image classification"],"prefix":"10.3389","volume":"13","author":[{"given":"Hongwei","family":"Chang","sequence":"first","affiliation":[{"name":"Shanxi Vocational University of Culture and Tourism, Institute of Digital Technology","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Life Sciences and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1177\/08953996251317416","article-title":"Multi-axis transformer based U-net with class balanced ensemble model for lung disease classification using x-ray images","volume":"33","author":"Maruthai","year":"2025","journal-title":"J X-ray Sci Technol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"102032","DOI":"10.1016\/j.media.2021.102032","article-title":"Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides","volume":"70","author":"Riasatian","year":"2021","journal-title":"Med Image Anal."},{"key":"B3","doi-asserted-by":"publisher","first-page":"e3934","DOI":"10.7717\/peerj-cs.3934","article-title":"DPFD-net: a dual-path focus diffusion network for infrared small target detection","volume":"12","author":"Zong","year":"2026","journal-title":"Peerj Comput Sci"},{"key":"B4","doi-asserted-by":"publisher","first-page":"3687","DOI":"10.1038\/s41598-025-33178-1","article-title":"An efficient dual path deep learning framework for COVID-19 classification using lung CT scans with explainable AI","volume":"16","author":"Rahat","year":"2026","journal-title":"Sci Rep"},{"key":"B5","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1080\/0954898X.2024.2350579","article-title":"Enhancing multi-class lung disease classification in chest x-ray images: a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach","volume":"36","author":"Thavasimuthu","year":"2025","journal-title":"Netw-Comput Neural Syst"},{"key":"B6","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00354-025-00296-3","article-title":"Improved deep convolutional neural network with transfer learning based COVID-19 infection detection using CT image","volume":"43","author":"Ramakisan Varma","year":"2025","journal-title":"New Gener Comput."},{"key":"B7","doi-asserted-by":"publisher","first-page":"3159","DOI":"10.1007\/s10278-025-01760-8","article-title":"Lung disease classification with deep learning enhanced CNN architecture in chest X-ray imaging","volume":"39","author":"Slimani","year":"2026","journal-title":"J Imaging Inform Med."},{"key":"B8","doi-asserted-by":"publisher","first-page":"101669","DOI":"10.1016\/j.imu.2025.101669","article-title":"LMVT: a hybrid vision transformer with attention mechanisms for efficient and explainable lung cancer diagnosis","volume":"57","author":"Debnath","year":"2025","journal-title":"Inform Med Unlocked."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/technologies14010001","article-title":"Lung disease classification using deep learning and ROI-based chest X-ray images","volume":"14","author":"Nadal-Mart\u00ednez","year":"2026","journal-title":"Technol."},{"key":"B10","doi-asserted-by":"publisher","first-page":"023061","DOI":"10.1117\/1.JEI.34.2.023061","article-title":"Multigranularity feature aggregation in vision transformer for lung disease classification and segmentation","volume":"34","author":"Xu","year":"2025","journal-title":"J Electron Imaging"},{"key":"B11","doi-asserted-by":"publisher","first-page":"035233","DOI":"10.1088\/2631-8695\/adf02f","article-title":"A high-performance multimodal deep learning model for subtype classification of lung adenocarcinoma","volume":"7","author":"Cai","year":"2025","journal-title":"Eng Res Express."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1109\/SMC.2017.8122889","article-title":"Deep features for breast cancer histopathological image classification","author":"Spanhol","year":"2017","journal-title":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"},{"key":"B13","doi-asserted-by":"publisher","first-page":"6391804","DOI":"10.1049\/sfw2\/6391804","article-title":"Lung disease detection using hybrid CNN-XGBoost classifier with new loss function","volume":"2026","author":"Jafari Tabaghsar","year":"2026","journal-title":"IET Softw"},{"key":"B14","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s12880-025-01630-3","article-title":"Enhanced tuberculosis detection using vision transformers and explainable AI with a grad-CAM approach on chest x-rays","volume":"25","author":"Vanitha","year":"2025","journal-title":"BMC Med Imaging"},{"key":"B15","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/978-981-95-5699-1_12","article-title":"Multi-branch feature fusion network for medical image classification","volume-title":"Pattern Recognition and Computer Vision","author":"Liu","year":"2026"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2174\/0115734056388107250710120917","article-title":"Evaluation of deep learning methods for pulmonary disease classification","volume":"21","author":"Singh","year":"2025","journal-title":"Curr Med Imaging"},{"key":"B17","first-page":"436","volume-title":"Attentional Feature Fusion for Pulmonary X-ray Image Classification","author":"Bi","year":"2025"},{"key":"B18","doi-asserted-by":"publisher","first-page":"373","DOI":"10.14569\/IJACSA.2025.0160240","article-title":"Multi-classification convolution neural network models for chest disease classification","volume":"16","author":"Ayman","year":"2025","journal-title":"Int J Adv Comput Sci Appl"},{"key":"B19","author":"Soares","year":"2020"},{"key":"B20","doi-asserted-by":"publisher","first-page":"54040","DOI":"10.1109\/ACCESS.2025.3554184","article-title":"EFFResNet-ViT: a fusion-based convolutional and vision transformer model for explainable medical image classification","volume":"13","author":"Hussain","year":"2025","journal-title":"IEEE Access"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1186\/s12885-025-14320-8","article-title":"Deep learning-based lung cancer classification of CT images","volume":"25","author":"Faizi","year":"2025","journal-title":"BMC Cancer"},{"key":"B22","doi-asserted-by":"publisher","first-page":"23082","DOI":"10.1038\/s41598-025-07112-4","article-title":"A refined lion optimizer for deep learning","volume":"15","author":"Rong","year":"2025","journal-title":"Sci Rep"},{"key":"B23","doi-asserted-by":"publisher","first-page":"101672","DOI":"10.1016\/j.jrras.2025.101672","article-title":"A novel approach to integrating vision transformers and machine learning for robust lung nodule classification using CT imaging","volume":"18","author":"Huang","year":"2025","journal-title":"J Radiat Res Appl Sci"},{"key":"B24","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.chest.2023.10.012","article-title":"A deep learning-based radiomic classifier for usual interstitial pneumonia","volume":"165","author":"Chung","year":"2024","journal-title":"Chest"},{"key":"B25","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1080\/00051144.2024.2314918","article-title":"PulmonU-net: a semantic lung disease segmentation model leveraging the benefit of multiscale feature concatenation and leaky ReLU","volume":"65","author":"Shyni","year":"2024","journal-title":"Automatika"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e70262","DOI":"10.1002\/ima.70262","article-title":"Enhancing lung disease diagnosis: a high performance hybrid deep learning framework for multi-class chest x-ray analysis","volume":"36","author":"Bascetin","year":"2026","journal-title":"Int J Imaging Syst Technol"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2174\/0115734056326685250101113959","article-title":"Enhanced pneumonia detection in chest x-rays using hybrid convolutional and vision transformer networks","volume":"21","author":"Mustapha","year":"2025","journal-title":"Curr Med Imaging"},{"key":"B28","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s40009-024-01523-z","article-title":"Skin disease classification using deep learning","volume":"48","author":"Rangaswamy","year":"2025","journal-title":"Natl Acad Sci Lett"},{"key":"B29","doi-asserted-by":"publisher","first-page":"106902","DOI":"10.1016\/j.engappai.2023.106902","article-title":"Lung-EffNet: lung cancer classification using EfficientNet from CT-scan images","volume":"126","author":"Raza","year":"2023","journal-title":"Eng Appl Artif Intell."},{"key":"B30","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1177\/01617346251346060","article-title":"MVKD-trans: a multi-view knowledge distillation vision transformer architecture for breast cancer classification based on ultrasound images","volume":"47","author":"Ling","year":"2025","journal-title":"Ultrason Imaging"},{"key":"B31","doi-asserted-by":"publisher","first-page":"35127","DOI":"10.1038\/s41598-025-19114-3","article-title":"Transfer learning with fuzzy decision support for multi-class lung disease classification: performance analysis of pre-trained CNN models","volume":"15","author":"Yadav","year":"2025","journal-title":"Sci Rep."},{"key":"B32","doi-asserted-by":"publisher","first-page":"125553","DOI":"10.1016\/j.eswa.2024.125553","article-title":"Focalnext: a ConvNeXt augmented FocalNet architecture for lung cancer classification from CT-scan images","volume":"261","author":"Gulsoy","year":"2025","journal-title":"Expert Syst Appl"},{"key":"B33","doi-asserted-by":"publisher","first-page":"103507","DOI":"10.1016\/j.inffus.2025.103507","article-title":"DAFNet: a novel dynamic adaptive fusion network for medical image classification","volume":"126","author":"Cai","year":"2026","journal-title":"Inf Fusion"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1960913\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:37:31Z","timestamp":1790055451000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1960913\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,22]]},"references-count":33,"alternative-id":["10.3389\/fmed.2026.1960913"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1960913","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,22]]},"article-number":"1960913"}}