{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T07:04:10Z","timestamp":1779865450419,"version":"3.53.1"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.bspc.2026.110389","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T02:49:05Z","timestamp":1778467745000},"page":"110389","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["LAMobileNet: Lesion-associated classification-guided multi-scale hybrid network for efficient lung disease classification"],"prefix":"10.1016","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4250-6339","authenticated-orcid":false,"given":"Bo","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4291-5209","authenticated-orcid":false,"given":"Qingshan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5788-042X","authenticated-orcid":false,"given":"YanShan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijie","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhe","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunlong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongxiong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110389_b1","series-title":"The top 10 causes of death","author":"World Health Organization","year":"2020"},{"key":"10.1016\/j.bspc.2026.110389_b2","article-title":"Chest imaging appearance of COVID-19 infection","volume":"2","author":"Kong","year":"2020","journal-title":"Radiol.: Cardiothorac. Imaging"},{"key":"10.1016\/j.bspc.2026.110389_b3","doi-asserted-by":"crossref","first-page":"E982","DOI":"10.1055\/a-1839-4303","article-title":"Severe immune checkpoint inhibitor-associated gastritis: A case series and literature review","volume":"10","author":"Sugiyama","year":"2022","journal-title":"Endosc. Int. Open"},{"key":"10.1016\/j.bspc.2026.110389_b4","series-title":"CheXNet: Radiologist-level pneumonia detection on chest X-Rays with deep learning","author":"Rajpurkar","year":"2017"},{"issue":"5","key":"10.1016\/j.bspc.2026.110389_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e26938","article-title":"A hybrid deep learning CNN model for COVID-19 detection from chest X-rays","volume":"10","author":"Abdullah","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.bspc.2026.110389_b6","first-page":"1","article-title":"Deep learning in medical image analysis: A survey","volume":"vol. I","author":"D A","year":"2024"},{"key":"10.1016\/j.bspc.2026.110389_b7","series-title":"CBAM: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.bspc.2026.110389_b8","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.3390\/bioengineering10111249","article-title":"Multi-scale learning with sparse residual network for explainable multi-disease diagnosis in OCT images","volume":"10","author":"Bui","year":"2023","journal-title":"Bioengineering"},{"key":"10.1016\/j.bspc.2026.110389_b9","doi-asserted-by":"crossref","first-page":"15857","DOI":"10.1038\/s41598-021-94750-z","article-title":"Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance","volume":"11","author":"Schultheiss","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110389_b10","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.bspc.2026.110389_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102861","article-title":"Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images","volume":"88","author":"Dawood","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110389_b12","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.bspc.2026.110389_b13","series-title":"MobileNetV2: Inverted residuals and linear bottlenecks","author":"Sandler","year":"2019"},{"key":"10.1016\/j.bspc.2026.110389_b14","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1800","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"key":"10.1016\/j.bspc.2026.110389_b15","doi-asserted-by":"crossref","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","article-title":"COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"issue":"24","key":"10.1016\/j.bspc.2026.110389_b16","doi-asserted-by":"crossref","DOI":"10.3390\/app132413111","article-title":"A deep learning review of ResNet architecture for lung disease identification in CXR image","volume":"13","author":"Hasanah","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2026.110389_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2020.100360","article-title":"A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of xception and ResNet50V2","volume":"19","author":"Rahimzadeh","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"10.1016\/j.bspc.2026.110389_b18","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/9036457","article-title":"Lung disease classification in CXR images using hybrid inception-ResNet-v2 model and edge computing","author":"Sharma","year":"2022","journal-title":"J. Heal. Eng."},{"key":"10.1016\/j.bspc.2026.110389_b19","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110389_b20","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"10.1016\/j.bspc.2026.110389_b21","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.bspc.2026.110389_b22","series-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2020"},{"key":"10.1016\/j.bspc.2026.110389_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104348","article-title":"Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases","volume":"132","author":"Ibrahim","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110389_b24","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110389_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119503","article-title":"Detection and classification of pneumonia using novel superior exponential (SupEx) activation function in convolutional neural networks","volume":"217","author":"Kili\u00e7arslan","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110389_b26","article-title":"Vision-audio multimodal object recognition using hybrid and tensor fusion techniques","volume":"126, Part B","author":"Ahmed","year":"2026","journal-title":"Inf. Fusion"},{"issue":"8","key":"10.1016\/j.bspc.2026.110389_b27","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1109\/TMI.2020.2993291","article-title":"Deep learning COVID-19 features on CXR using limited training data sets","volume":"39","author":"Oh","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110389_b28","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.bspc.2026.110389_b29","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13708","article-title":"Coordinate attention for efficient mobile network design","author":"Hou","year":"2021"},{"key":"10.1016\/j.bspc.2026.110389_b30","series-title":"TransUNet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.bspc.2026.110389_b31","series-title":"Beyond self-attention: External attention using two linear layers for visual tasks","author":"Guo","year":"2021"},{"key":"10.1016\/j.bspc.2026.110389_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120961","article-title":"An efficient deep learning model using network pruning for fake banknote recognition","volume":"233","author":"Pach\u00f3n","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110389_b33","series-title":"2019 IEEE\/CVF International Conference on Computer Vision","first-page":"1314","article-title":"Searching for MobileNetV3","author":"Howard","year":"2019"},{"key":"10.1016\/j.bspc.2026.110389_b34","doi-asserted-by":"crossref","DOI":"10.3390\/biomedinformatics4020054","article-title":"Advancing early leukemia diagnostics: A comprehensive study incorporating image processing and transfer learning","author":"Haque","year":"2024","journal-title":"BioMedInformatics"},{"issue":"6","key":"10.1016\/j.bspc.2026.110389_b35","doi-asserted-by":"crossref","first-page":"651","DOI":"10.3390\/bioengineering12060651","article-title":"Hierarchical swin transformer ensemble with explainable AI for robust and decentralized breast cancer diagnosis","volume":"12","author":"Ahmed","year":"2025","journal-title":"Bioengineering"},{"key":"10.1016\/j.bspc.2026.110389_b36","author":"Kaggle"},{"key":"10.1016\/j.bspc.2026.110389_b37","author":"Chowdhury"},{"key":"10.1016\/j.bspc.2026.110389_b38","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI help in screening viral and COVID-19 pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110389_b39","author":"Kaggle"},{"key":"10.1016\/j.bspc.2026.110389_b40","author":"Fernando"},{"key":"10.1016\/j.bspc.2026.110389_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.colsurfb.2025.114605","article-title":"Polymeric nanocomposites in a biological interface: From a molecular view to final applications","volume":"251","author":"Schmidt","year":"2025","journal-title":"Colloids Surf. B"},{"key":"10.1016\/j.bspc.2026.110389_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.combustflame.2025.114411","article-title":"Ignition threshold and emission characteristics of self-sustaining smoldering combustion","volume":"281","author":"Chen","year":"2025","journal-title":"Combust. Flame"},{"key":"10.1016\/j.bspc.2026.110389_b43","series-title":"Advanced chest X-Ray analysis via transformer-based image descriptors and cross-model attention mechanism","author":"Agarwal","year":"2025"},{"key":"10.1016\/j.bspc.2026.110389_b44","series-title":"Mish: A self regularized non-monotonic activation function","author":"Misra","year":"2020"},{"key":"10.1016\/j.bspc.2026.110389_b45","doi-asserted-by":"crossref","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":"Informatics Med. Unlocked"},{"key":"10.1016\/j.bspc.2026.110389_b46","series-title":"2017 IEEE International Conference on Computer Vision","first-page":"2999","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009432?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009432?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T06:23:32Z","timestamp":1779863012000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":46,"alternative-id":["S1746809426009432"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110389","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"LAMobileNet: Lesion-associated classification-guided multi-scale hybrid network for efficient lung disease classification","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110389","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110389"}}