{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:28:33Z","timestamp":1762514913325,"version":"build-2065373602"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T00:00:00Z","timestamp":1754697600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T00:00:00Z","timestamp":1754697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s13042-025-02746-2","type":"journal-article","created":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T03:59:49Z","timestamp":1754711989000},"page":"9127-9144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FETransNet: an enhanced lung disease classification approach combining EfficientNet and transformer with adaptive focal loss"],"prefix":"10.1007","volume":"16","author":[{"given":"Zhentao","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqiang","family":"Mei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,9]]},"reference":[{"issue":"10","key":"2746_CR1","doi-asserted-by":"publisher","first-page":"2752","DOI":"10.1093\/cid\/ciaa654","volume":"71","author":"HV Huff","year":"2020","unstructured":"Huff HV, Singh A (2020) Asymptomatic transmission during the coronavirus disease 2019 pandemic and implications for public health strategies. Clin Infect Dis 71(10):2752\u20132756","journal-title":"Clin Infect Dis"},{"issue":"7","key":"2746_CR2","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1002\/jmv.25766","volume":"92","author":"F He","year":"2020","unstructured":"He F, Deng Y, Li W (2020) Coronavirus disease 2019: what we know? J Med Virol 92(7):719\u2013725","journal-title":"J Med Virol"},{"key":"2746_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120528","volume":"229","author":"M Nahiduzzaman","year":"2023","unstructured":"Nahiduzzaman M, Goni MOF, Hassan R, Islam MR, Syfullah MK, Shahriar SM, Anower MS, Ahsan M, Haider J, Kowalski M (2023) Parallel cnn-elm: a multiclass classification of chest x-ray images to identify seventeen lung diseases including COVID-19. Expert Syst Appl 229:120528","journal-title":"Expert Syst Appl"},{"issue":"9773","key":"2746_CR4","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1016\/S0140-6736(10)61459-6","volume":"377","author":"O Ruuskanen","year":"2011","unstructured":"Ruuskanen O, Lahti E, Jennings LC, Murdoch DR (2011) Viral pneumonia. Lancet 377(9773):1264\u20131275","journal-title":"Lancet"},{"key":"2746_CR5","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.aej.2024.04.072","volume":"98","author":"AN Patel","year":"2024","unstructured":"Patel AN, Murugan R, Srivastava G, Maddikunta PKR, Yenduri G, Gadekallu TR, Chengoden R (2024) An explainable transfer learning framework for multi-classification of lung diseases in chest x-rays. Alex Eng J 98:328\u2013343","journal-title":"Alex Eng J"},{"key":"2746_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106646","volume":"155","author":"FJM Shamrat","year":"2023","unstructured":"Shamrat FJM, Azam S, Karim A, Ahmed K, Bui FM, De Boer F (2023) High-precision multiclass classification of lung disease through customized mobilenetv2 from chest x-ray images. Comput Biol Med 155:106646","journal-title":"Comput Biol Med"},{"key":"2746_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.clim.2020.108448","volume":"215","author":"S Felsenstein","year":"2020","unstructured":"Felsenstein S, Herbert JA, McNamara PS, Hedrich CM (2020) COVID-19: immunology and treatment options. Clin Immunol 215:108448","journal-title":"Clin Immunol"},{"key":"2746_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107053","volume":"225","author":"Y Chen","year":"2022","unstructured":"Chen Y, Lin Y, Xu X, Ding J, Li C, Zeng Y, Liu W, Xie W, Huang J (2022) Classification of lungs infected COVID-19 images based on inception-resnet. Comput Methods Programs Biomed 225:107053","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"2746_CR9","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.athoracsur.2016.06.061","volume":"103","author":"MK Kamel","year":"2017","unstructured":"Kamel MK, Rahouma M, Ghaly G, Nasar A, Port JL, Stiles BM, Nguyen AB, Altorki NK, Lee PC (2017) Clinical predictors of persistent mediastinal nodal disease after induction therapy for stage iiia n2 non-small cell lung cancer. Ann Thorac Surg 103(1):281\u2013286","journal-title":"Ann Thorac Surg"},{"issue":"4","key":"2746_CR10","doi-asserted-by":"publisher","first-page":"125","DOI":"10.18100\/ijamec.799363","volume":"8","author":"M Aykanat","year":"2020","unstructured":"Aykanat M, K\u0131l\u0131\u00e7 \u00d6, Kurt B, Saryal SB (2020) Lung disease classification using machine learning algorithms. Int J Appl Math Electron Comput 8(4):125\u2013132","journal-title":"Int J Appl Math Electron Comput"},{"key":"2746_CR11","doi-asserted-by":"crossref","unstructured":"Sriporn K, Tsai C-F, Tsai C-E, Wang P (2020) Analyzing lung disease using highly effective deep learning techniques. In: Healthcare, vol 8. MDPI, pp 107","DOI":"10.3390\/healthcare8020107"},{"key":"2746_CR12","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV). IEEE, Montreal, Canada, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2746_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119560","volume":"217","author":"J Ding","year":"2023","unstructured":"Ding J, Li W, Pei L, Yang M, Ye C, Yuan B (2023) Sw-yolox: an anchor-free detector based transformer for sea surface object detection. Expert Syst Appl 217:119560","journal-title":"Expert Syst Appl"},{"issue":"9","key":"2746_CR14","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1007\/s10661-023-11628-5","volume":"195","author":"A Kumar","year":"2023","unstructured":"Kumar A, Yadav DP, Kumar D, Pant M, Pant G (2023) Multi-scale feature fusion-based lightweight dual stream transformer for detection of paddy leaf disease. Environ Monit Assess 195(9):1020","journal-title":"Environ Monit Assess"},{"issue":"1","key":"2746_CR15","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.compmedimag.2011.06.002","volume":"36","author":"U Ba\u011fc\u0131","year":"2012","unstructured":"Ba\u011fc\u0131 U, Bray M, Caban J, Yao J, Mollura DJ (2012) Computer-assisted detection of infectious lung diseases: a review. Comput Med Imaging Graph 36(1):72\u201384","journal-title":"Comput Med Imaging Graph"},{"issue":"23","key":"2746_CR16","doi-asserted-by":"publisher","first-page":"36479","DOI":"10.1007\/s11042-023-14960-7","volume":"82","author":"DP Yadav","year":"2023","unstructured":"Yadav DP, Jalal AS, Goyal A, Mishra A, Uprety K, Guragai N (2023) COVID-19 radiograph prognosis using a deep cresnext network. Multimed Tools Appl 82(23):36479\u201336505","journal-title":"Multimed Tools Appl"},{"key":"2746_CR17","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105\u20136114"},{"key":"2746_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106691","volume":"96","author":"G Marques","year":"2020","unstructured":"Marques G, Agarwal D, Torre D\u00edez I (2020) Automated medical diagnosis of COVID-19 through efficientnet convolutional neural network. Appl Soft Comput 96:106691","journal-title":"Appl Soft Comput"},{"key":"2746_CR19","unstructured":"Chowdhury NK, Kabir MA, Rahman MM, Rezoana N (2020) Ecovnet: an ensemble of deep convolutional neural networks based on efficientnet to detect COVID-19 from chest x-rays. arXiv preprint arXiv:2009.11850"},{"issue":"1","key":"2746_CR20","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3390\/axioms11010034","volume":"11","author":"DR Nayak","year":"2022","unstructured":"Nayak DR, Padhy N, Mallick PK, Zymbler M, Kumar S (2022) Brain tumor classification using dense efficient-net. Axioms 11(1):34","journal-title":"Axioms"},{"key":"2746_CR21","doi-asserted-by":"crossref","unstructured":"Alquzi S, Alhichri H, Bazi Y (2022) Detection of COVID-19 using efficientnet-b3 cnn and chest computed tomography images. In: International conference on innovative computing and communications: proceedings of ICICC 2021, vol 1. Springer, pp 365\u2013373","DOI":"10.1007\/978-981-16-2594-7_30"},{"key":"2746_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106902","volume":"126","author":"R Raza","year":"2023","unstructured":"Raza R, Zulfiqar F, Khan MO, Arif M, Alvi A, Iftikhar MA, Alam T (2023) Lung-effnet: lung cancer classification using efficientnet from ct-scan images. Eng Appl Artif Intell 126:106902","journal-title":"Eng Appl Artif Intell"},{"key":"2746_CR23","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30. pp 5998\u20136008"},{"key":"2746_CR24","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16 x 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"2746_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105939","volume":"149","author":"C Xin","year":"2022","unstructured":"Xin C, Liu Z, Zhao K, Miao L, Ma Y, Zhu X, Zhou Q, Wang S, Li L, Yang F (2022) An improved transformer network for skin cancer classification. Comput Biol Med 149:105939","journal-title":"Comput Biol Med"},{"issue":"9","key":"2746_CR26","doi-asserted-by":"publisher","first-page":"6713","DOI":"10.1007\/s00521-022-08053-z","volume":"35","author":"S Ayas","year":"2023","unstructured":"Ayas S (2023) Multiclass skin lesion classification in dermoscopic images using swin transformer model. Neural Comput Appl 35(9):6713\u20136722","journal-title":"Neural Comput Appl"},{"key":"2746_CR27","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1109\/JBHI.2023.3247949","volume":"28","author":"S Chen","year":"2023","unstructured":"Chen S, Ren S, Wang G, Huang M, Xue C (2023) Interpretable cnn-multilevel attention transformer for rapid recognition of pneumonia from chest x-ray images. IEEE J Biomed Health Inform 28:753\u2013764","journal-title":"IEEE J Biomed Health Inform"},{"key":"2746_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.113137","volume":"218","author":"DP Yadav","year":"2023","unstructured":"Yadav DP, Chauhan S, Kada B, Kumar A (2023) Spatial attention-based dual stream transformer for concrete defect identification. Measurement 218:113137","journal-title":"Measurement"},{"key":"2746_CR29","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision (ICCV). IEEE, Venice, Italy, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"2746_CR30","unstructured":"Phan TH, Yamamoto K (2020) Resolving class imbalance in object detection with weighted cross entropy losses. arXiv preprint arXiv: 2006.01413"},{"key":"2746_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102035","volume":"71","author":"J Ma","year":"2021","unstructured":"Ma J, Chen J, Ng M, Huang R, Li Y, Li C, Yang X, Martel AL (2021) Loss odyssey in medical image segmentation. Med Image Anal 71:102035","journal-title":"Med Image Anal"},{"key":"2746_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.102026","volume":"95","author":"M Yeung","year":"2022","unstructured":"Yeung M, Sala E, Sch\u00f6nlieb C-B, Rundo L (2022) Unified focal loss: generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Comput Med Imaging Graph 95:102026","journal-title":"Comput Med Imaging Graph"},{"key":"2746_CR33","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Nashville, TN, USA, pp 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"2746_CR34","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, et al (2018) Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv: 1804.03999"},{"issue":"5","key":"2746_CR35","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122\u20131131","journal-title":"Cell"},{"issue":"2","key":"2746_CR36","first-page":"651","volume":"2","author":"D Kermany","year":"2018","unstructured":"Kermany D, Zhang K, Goldbaum M (2018) Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley Data 2(2):651","journal-title":"Mendeley Data"},{"key":"2746_CR37","doi-asserted-by":"publisher","first-page":"191586","DOI":"10.1109\/ACCESS.2020.3031384","volume":"8","author":"T Rahman","year":"2020","unstructured":"Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, Hamid T, Islam MT, Kashem S, Mahbub ZB (2020) Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization. IEEE Access 8:191586\u2013191601","journal-title":"IEEE Access"},{"key":"2746_CR38","doi-asserted-by":"crossref","unstructured":"He X, Yang X, Zhang S, Zhao J, Zhang Y, Xing E, Xie P (2020) Sample-efficient deep learning for covid-19 diagnosis based on ct scans. medrxiv, 2020-04","DOI":"10.1101\/2020.04.13.20063941"},{"key":"2746_CR39","doi-asserted-by":"crossref","unstructured":"Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M (2020) COVID-19 image data collection: prospective predictions are the future. arXiv preprint arXiv:2006.11988","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"2746_CR40","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR). IEEE, New Orleans, LA, USA, pp 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02746-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02746-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02746-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:23:34Z","timestamp":1762514614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02746-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,9]]},"references-count":40,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["2746"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02746-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,8,9]]},"assertion":[{"value":"14 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}