{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:25:22Z","timestamp":1783023922396,"version":"3.54.6"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110802","type":"journal-article","created":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T16:19:07Z","timestamp":1781453947000},"page":"110802","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Quad head attention fused Deep learning model for Coronavirus Disease-19 classification"],"prefix":"10.1016","volume":"125","author":[{"given":"Vanshika","family":"Rastogi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanthi","family":"M","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anand","family":"R","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.bspc.2026.110802_b0005","doi-asserted-by":"crossref","first-page":"192","DOI":"10.3390\/bdcc8120192","article-title":"Integrating Statistical Methods and Machine Learning Techniques to Analyze and Classify COVID-19 Symptom Severity","volume":"8","author":"Raddad","year":"2024","journal-title":"Big Data and Cognitive Computing."},{"issue":"4","key":"10.1016\/j.bspc.2026.110802_b0010","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.jacr.2020.02.008","article-title":"Coronavirus (COVID-19) outbreak: what the department of radiology should know","volume":"17","author":"Kooraki","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"10.1016\/j.bspc.2026.110802_b0015","first-page":"369","article-title":"Clinical features of covid-19","volume":"17","author":"Vetter","year":"2020","journal-title":"BMJ"},{"issue":"8","key":"10.1016\/j.bspc.2026.110802_b0020","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1080\/0952813X.2023.2165724","article-title":"A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron","volume":"36","author":"Khan","year":"2024","journal-title":"J. Exp. Theor. Artif. Intell."},{"issue":"3","key":"10.1016\/j.bspc.2026.110802_b0025","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1007\/s10489-020-01862-6","article-title":"COVID-19 open source data sets: a comprehensive survey","volume":"51","author":"Shuja","year":"2021","journal-title":"Appl. Intell."},{"issue":"9","key":"10.1016\/j.bspc.2026.110802_b0030","doi-asserted-by":"crossref","first-page":"84783","DOI":"10.1109\/ACCESS.2021.3085682","article-title":"COVID-Scraper: an open-source toolset for automatically scraping and processing global multi-scale spatiotemporal COVID-19 records","volume":"3","author":"Lan","year":"2021","journal-title":"IEEE Access"},{"issue":"20","key":"10.1016\/j.bspc.2026.110802_b0035","article-title":"A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images","volume":"1","author":"Islam","year":"2020","journal-title":"Inf. Med. Unlocked"},{"issue":"8","key":"10.1016\/j.bspc.2026.110802_b0040","first-page":"1583","article-title":"Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine","volume":"23","author":"Singh","year":"2020","journal-title":"J. Discret. Math. Sci. Cryptogr."},{"issue":"6","key":"10.1016\/j.bspc.2026.110802_b0045","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.3390\/s22062224","article-title":"An ensemble learning model for COVID-19 detection from blood test samples","volume":"22","author":"Abayomi-Alli","year":"2022","journal-title":"Sensors"},{"issue":"9","key":"10.1016\/j.bspc.2026.110802_b0050","doi-asserted-by":"crossref","first-page":"134384","DOI":"10.1109\/ACCESS.2021.3114364","article-title":"Application of hidden Markov models to analyze, group and visualize spatio-temporal COVID-19 data","volume":"22","author":"Zhou","year":"2021","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.bspc.2026.110802_b0055","doi-asserted-by":"crossref","first-page":"80","DOI":"10.29040\/ijcis.v2i3.41","article-title":"Prediction system for the spread of Corona virus in Central Java with K-nearest neighbor (KNN) method","volume":"2","author":"Fitriyadi","year":"2021","journal-title":"International Journal of Computer and Information System (IJCIS)."},{"issue":"3","key":"10.1016\/j.bspc.2026.110802_b0060","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","article-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","volume":"24","author":"Narin","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"10.1016\/j.bspc.2026.110802_b0065","doi-asserted-by":"crossref","unstructured":"Huang L, Ruan S, Denoeux T. Covid-19 classification with deep neural network and belief functions. InThe Fifth International Conference on Biological Information and Biomedical Engineering 2021 Jul 20 (pp. 1-4).","DOI":"10.1145\/3469678.3469719"},{"issue":"4","key":"10.1016\/j.bspc.2026.110802_b0070","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1007\/s00530-021-00826-1","article-title":"Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images","volume":"28","author":"Ravi","year":"2022","journal-title":"Multimedia Syst."},{"issue":"1","key":"10.1016\/j.bspc.2026.110802_b0075","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1186\/s12245-024-00681-7","article-title":"Development of decision tree classification algorithms in predicting mortality of COVID-19 patients","volume":"17","author":"Mohammadi-Pirouz","year":"2024","journal-title":"Int J Emerg Med"},{"issue":"9","key":"10.1016\/j.bspc.2026.110802_b0080","doi-asserted-by":"crossref","first-page":"6371","DOI":"10.1007\/s11042-024-19124-9","article-title":"Enhancing early detection of covid-19 with machine learning and blood test results","volume":"84","author":"El Gannour","year":"2025","journal-title":"Multimed. Tools Appl."},{"issue":"4","key":"10.1016\/j.bspc.2026.110802_b0085","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1007\/s12559-020-09787-5","article-title":"Pneumonia classification using deep learning from chest X-ray images during COVID-19","volume":"16","author":"Ibrahim","year":"2024","journal-title":"Cogn. Comput."},{"issue":"1","key":"10.1016\/j.bspc.2026.110802_b0090","doi-asserted-by":"crossref","first-page":"22673","DOI":"10.1038\/s41598-024-74057-5","article-title":"Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections","volume":"14","author":"Zhou","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bspc.2026.110802_b0095","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1038\/s41598-024-84644-1","article-title":"AI based medical imagery diagnosis for COVID-19 disease examination and remedy","volume":"15","author":"Aboshosha","year":"2025","journal-title":"Sci. Rep."},{"issue":"279","key":"10.1016\/j.bspc.2026.110802_b0100","article-title":"Spectrochemical and explainable artificial intelligence approaches for molecular level identification of the status of critically ill patients with COVID-19","volume":"1","author":"Tokgoz","year":"2024","journal-title":"Talanta"},{"issue":"6","key":"10.1016\/j.bspc.2026.110802_b0105","article-title":"Covid-19 detection from chest ct images using optimized deep features and ensemble classification","volume":"1","author":"Hossain","year":"2024","journal-title":"Syst. Soft Comput."},{"key":"10.1016\/j.bspc.2026.110802_b0110","article-title":"A vision transformer machine learning model for COVID-19 diagnosis using chest X-ray images","volume":"5","author":"Chen","year":"2024","journal-title":"Healthcare Anal."},{"issue":"4","key":"10.1016\/j.bspc.2026.110802_b0115","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1016\/j.bbe.2021.10.004","article-title":"WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images","volume":"41","author":"Murugan","year":"2021","journal-title":"Biocybernetics and Biomedical Engineering"},{"issue":"3","key":"10.1016\/j.bspc.2026.110802_b0120","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1007\/s12652-022-04508-2","article-title":"Development of CNN-LSTM combinational architecture for COVID-19 detection","volume":"14","author":"Narula","year":"2023","journal-title":"J. Ambient Intell. Hum. Comput."},{"issue":"3","key":"10.1016\/j.bspc.2026.110802_b0125","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1007\/s10489-020-01904-z","article-title":"OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19","volume":"51","author":"Goel","year":"2021","journal-title":"Appl. Intell."},{"key":"10.1016\/j.bspc.2026.110802_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106742","article-title":"An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization","volume":"98","author":"Ezzat","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.bspc.2026.110802_b0135","article-title":"COVID-19 classification using chest X-ray images: a framework of CNN-LSTM and improved max value moth flame optimization","volume":"10","author":"Hamza","year":"2022","journal-title":"Front. Public Health"},{"issue":"4","key":"10.1016\/j.bspc.2026.110802_b0140","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1007\/s12559-021-09848-3","article-title":"COVID-19 infection detection from chest X-ray images using hybrid social group optimization and support vector classifier","volume":"16","author":"Singh","year":"2024","journal-title":"Cogn. Comput."},{"issue":"26","key":"10.1016\/j.bspc.2026.110802_b0145","doi-asserted-by":"crossref","first-page":"41073","DOI":"10.1007\/s11042-023-15031-7","article-title":"Optimal feature selection for COVID-19 detection with CT images enabled by metaheuristic optimization and artificial intelligence","volume":"82","author":"Torse","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.bspc.2026.110802_b0150","unstructured":"COVID-19 Blood Test Database: https:\/\/github.com\/MachineLearnia\/Python-Machine-Learning\/blob\/master\/Dataset\/dataset.csv, accessed on June 2025."},{"key":"10.1016\/j.bspc.2026.110802_b0155","unstructured":"COVID-19 Symptoms Database: https:\/\/www.kaggle.com\/code\/mithilesh16\/covid-19-symptom-analysis\/notebook, accessed on June 2025."},{"key":"10.1016\/j.bspc.2026.110802_b0160","unstructured":"Choudhury A, Kosorok MR. Missing data imputation for classification problems. arXiv preprint arXiv:2002.10709. 2020 Feb 25."},{"issue":"10","key":"10.1016\/j.bspc.2026.110802_b0165","doi-asserted-by":"crossref","first-page":"30655","DOI":"10.1109\/ACCESS.2022.3158977","article-title":"SMOTified-GAN for class imbalanced pattern classification problems","volume":"11","author":"Sharma","year":"2022","journal-title":"IEEE Access"},{"issue":"10","key":"10.1016\/j.bspc.2026.110802_b0170","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1109\/JBHI.2020.3001216","article-title":"Deep sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: NLP using LSTM recurrent neural network approach","volume":"24","author":"Jelodar","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"177","key":"10.1016\/j.bspc.2026.110802_b0175","article-title":"Deep Belief Network based audio classification for construction sites monitoring","volume":"1","author":"Scarpiniti","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110802_b0180","doi-asserted-by":"crossref","unstructured":"Sae-Lim W, Wettayaprasit W, Aiyarak P. Leukemia classification using deep belief network [Internet]. 2013.","DOI":"10.2316\/P.2013.793-043"},{"key":"10.1016\/j.bspc.2026.110802_b0185","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS. CBAM: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) 2018 (pp. 3-19).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.bspc.2026.110802_b0190","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.bspc.2026.110802_b0195","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W. Ccnet: Criss-cross attention for semantic segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision 2019 (pp. 603-612).","DOI":"10.1109\/ICCV.2019.00069"},{"key":"10.1016\/j.bspc.2026.110802_b0200","doi-asserted-by":"crossref","unstructured":"Shaw P, Uszkoreit J, Vaswani A. Self-attention with relative position representations. arXiv preprint arXiv:1803.02155. 2018 Mar 6.","DOI":"10.18653\/v1\/N18-2074"},{"key":"10.1016\/j.bspc.2026.110802_b0205","article-title":"Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images","volume":"27","author":"Motamed","year":"2021","journal-title":"Inf. Med. Unlocked"},{"issue":"4","key":"10.1016\/j.bspc.2026.110802_b0210","article-title":"Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning","volume":"2","author":"Islam","year":"2022","journal-title":"BenchCouncil Trans. Benchmarks Stand. Eval."},{"key":"10.1016\/j.bspc.2026.110802_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.121141","article-title":"OPT-CO: optimizing pre-trained transformer models for efficient COVID-19 classification with stochastic configuration networks","volume":"680","author":"Zhu","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.bspc.2026.110802_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103860","article-title":"CXGNet: a tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer","volume":"77","author":"Gopatoti","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"10.1016\/j.bspc.2026.110802_b0225","first-page":"2183","article-title":"Detection and classification of COVID-19 disease using SWHO-based deep neural network classifier","volume":"11","author":"Rastogi","year":"2023","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942601356X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942601356X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:59:54Z","timestamp":1783022394000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S174680942601356X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":45,"alternative-id":["S174680942601356X"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110802","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Quad head attention fused Deep learning model for Coronavirus Disease-19 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.2026.110802","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":"110802"}}