{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:29:14Z","timestamp":1740122954756,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16983-6","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T08:01:52Z","timestamp":1696924912000},"page":"40773-40790","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["$$\\mathrm SRC_{2}$$: a novel deep learning based technique for identifying COVID-19 using images of chest x-ray"],"prefix":"10.1007","volume":"83","author":[{"given":"Utsav","family":"Acharya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shashwati","family":"Banerjea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2771-9950","authenticated-orcid":false,"given":"Rajitha","family":"B","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,10]]},"reference":[{"key":"16983_CR1","doi-asserted-by":"publisher","first-page":"102433","DOI":"10.1016\/j.jaut.2020.102433","volume":"109","author":"HA Rothan","year":"2020","unstructured":"Rothan HA, Byrareddy SN (2020) The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun 109:102433","journal-title":"J Autoimmun"},{"issue":"4","key":"16983_CR2","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.jds.2020.02.002","volume":"15","author":"H Guo","year":"2020","unstructured":"Guo H et al (2020) The impact of the COVID-19 epidemic on the utilization of emergency dental services. J Dent Sci 15(4):564\u2013567","journal-title":"J Dent Sci"},{"doi-asserted-by":"crossref","unstructured":"Liu C et al (2017) TX-CNN: detecting tuberculosis in chest X-ray images using convolutional neural network. 2017 IEEE international conference on image processing (ICIP). IEEE","key":"16983_CR3","DOI":"10.1109\/ICIP.2017.8296695"},{"doi-asserted-by":"crossref","unstructured":"Kesim E, Dokur Z, Olmez T (2019) X-ray chest image classification by a small-sized convolutional neural network. In: 2019 scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT). IEEE, pp 1\u20135","key":"16983_CR4","DOI":"10.1109\/EBBT.2019.8742050"},{"doi-asserted-by":"crossref","unstructured":"Dong Y et al (2017) Learning to read chest X-ray images from 16000+ examples using CNN. In: 2017 IEEE\/ACM international conference on connected health: applications, systems and engineering technologies (CHASE). IEEE","key":"16983_CR5","DOI":"10.1109\/CHASE.2017.59"},{"key":"16983_CR6","doi-asserted-by":"publisher","first-page":"4466","DOI":"10.1109\/ACCESS.2018.2885997","volume":"7","author":"S Xu","year":"2018","unstructured":"Xu S, Wu H, Bie R (2018) CXNet-m1: anomaly detection on chest X-rays with image-based deep learning. IEEE Access 7:4466\u20134477","journal-title":"IEEE Access"},{"issue":"2","key":"16983_CR7","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3390\/app10020559","volume":"10","author":"V Chouhan","year":"2020","unstructured":"Chouhan V et al (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci 10(2):559","journal-title":"Appl Sci"},{"issue":"11","key":"16983_CR8","doi-asserted-by":"publisher","first-page":"e1002686","DOI":"10.1371\/journal.pmed.1002686","volume":"15","author":"P Rajpurkar","year":"2018","unstructured":"Rajpurkar P et al (2018) Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 15(11):e1002686","journal-title":"PLoS Med"},{"key":"16983_CR9","doi-asserted-by":"publisher","first-page":"32510","DOI":"10.1109\/ACCESS.2019.2903587","volume":"7","author":"W Zuo","year":"2019","unstructured":"Zuo W et al (2019) Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection. IEEE Access 7:32510\u201332521","journal-title":"IEEE Access"},{"key":"16983_CR10","doi-asserted-by":"publisher","first-page":"608525","DOI":"10.3389\/fmed.2020.608525","volume":"7","author":"H Gunraj","year":"2020","unstructured":"Gunraj H, Wang L, Wong A (2020) Covidnet-ct: a tailored deep convolutional neural network design for detection of covid-19 cases from chest ct images. Front Med 7:608525","journal-title":"Front Med"},{"unstructured":"Cohen JP, Morrison P, Dao L (2020) COVID-19 image data collection. arXiv:2003.11597","key":"16983_CR11"},{"unstructured":"Kaggle (2020) Chest X-Ray images (Pneumonia) dataset. https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia","key":"16983_CR12"},{"doi-asserted-by":"crossref","unstructured":"Maghdid HS et al (2021) Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. Multimodal image exploitation and learning 2021. SPIE, vol 11734","key":"16983_CR13","DOI":"10.1117\/12.2588672"},{"unstructured":"Ghoshal B, Tucker A (2020) Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv:2003.10769","key":"16983_CR14"},{"key":"16983_CR15","doi-asserted-by":"publisher","first-page":"103182","DOI":"10.1016\/j.bspc.2021.103182","volume":"71","author":"A Bhattacharyya","year":"2022","unstructured":"Bhattacharyya A et al (2022) A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images. Biomed Signal Process Control 71:103182","journal-title":"Biomed Signal Process Control"},{"key":"16983_CR16","doi-asserted-by":"publisher","first-page":"119900","DOI":"10.1016\/j.eswa.2023.119900","volume":"223","author":"HI Hussein","year":"2023","unstructured":"Hussein HI et al (2023) Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images. Expert Syst Appl 223:119900","journal-title":"Expert Syst Appl"},{"key":"16983_CR17","doi-asserted-by":"publisher","first-page":"103977","DOI":"10.1016\/j.bspc.2022.103977","volume":"78","author":"ME Sahin","year":"2022","unstructured":"Sahin ME (2022) Deep learning-based approach for detecting COVID-19 in chest X-rays. Biomed Signal Process Control 78:103977","journal-title":"Biomed Signal Process Control"},{"key":"16983_CR18","doi-asserted-by":"publisher","first-page":"100096","DOI":"10.1016\/j.health.2022.100096","volume":"2","author":"O Ukwandu","year":"2022","unstructured":"Ukwandu O, Hindy H, Ukwandu E (2022) An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics. Healthc Anal 2:100096","journal-title":"Healthc Anal"},{"issue":"1","key":"16983_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bbe.2022.11.003","volume":"43","author":"GS George","year":"2023","unstructured":"George GS et al (2023) COVID-19 detection on chest X-ray images using homomorphic transformation and VGG inspired deep convolutional neural network. Biocybern Biomed Eng 43(1):1\u201316","journal-title":"Biocybern Biomed Eng"},{"key":"16983_CR20","doi-asserted-by":"publisher","first-page":"106859","DOI":"10.1016\/j.asoc.2020.106859","volume":"99","author":"A Gupta","year":"2021","unstructured":"Gupta A, Gupta S, Katarya R (2021) InstaCovNet-19: a deep learning classification model for the detection of COVID-19 patients using chest x-ray. Appl Soft Comput 99:106859","journal-title":"Appl Soft Comput"},{"key":"16983_CR21","doi-asserted-by":"publisher","first-page":"104454","DOI":"10.1016\/j.compbiomed.2021.104454","volume":"134","author":"PK Chaudhary","year":"2021","unstructured":"Chaudhary PK, Pachori RB (2021) FBSED based automatic diagnosis of COVID-19 using X-ray and CT images. Comput Biol Med 134:104454","journal-title":"Comput Biol Med"},{"key":"16983_CR22","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1007\/s10489-020-01867-1","volume":"51","author":"T Zebin","year":"2021","unstructured":"Zebin T, Rezvy S (2021) COVID-19 detection and disease progression visualization: deep learning on chest X-rays for classification and coarse localization. Appl Intell 51:1010\u20131021","journal-title":"Appl Intell"},{"unstructured":"Howard AG et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861","key":"16983_CR23"},{"doi-asserted-by":"crossref","unstructured":"Szegedy C et al (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition","key":"16983_CR24","DOI":"10.1109\/CVPR.2016.308"},{"doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition","key":"16983_CR25","DOI":"10.1109\/CVPR.2017.195"},{"doi-asserted-by":"crossref","unstructured":"He K et al (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition","key":"16983_CR26","DOI":"10.1109\/CVPR.2016.90"},{"key":"16983_CR27","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T et al (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792","journal-title":"Comput Biol Med"},{"key":"16983_CR28","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","volume":"43","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43:635\u2013640","journal-title":"Phys Eng Sci Med"},{"doi-asserted-by":"crossref","unstructured":"Sethy PK, Behera SK (2020) Detection of coronavirus disease (covid-19) based on deep features","key":"16983_CR29","DOI":"10.20944\/preprints202003.0300.v1"},{"unstructured":"Hemdan EE, Shouman MA, Karar ME (2020) Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv:2003.11055","key":"16983_CR30"},{"issue":"1","key":"16983_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci Rep 10(1):1\u201312","journal-title":"Sci Rep"},{"key":"16983_CR32","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1007\/s10489-020-01902-1","volume":"51","author":"R Jain","year":"2021","unstructured":"Jain R et al (2021) Deep learning based detection and analysis of COVID-19 on chest X-ray images. Appl Intell 51:1690\u20131700","journal-title":"Appl Intell"},{"key":"16983_CR33","doi-asserted-by":"publisher","first-page":"108711","DOI":"10.1016\/j.compeleceng.2023.108711","volume":"108","author":"R Soundrapandiyan","year":"2023","unstructured":"Soundrapandiyan R et al (2023) AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images. Comput Electr Eng 108:108711","journal-title":"Comput Electr Eng"},{"key":"16983_CR34","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.inffus.2020.10.004","volume":"67","author":"S-H Wang","year":"2021","unstructured":"Wang S-H et al (2021) Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inf Fusion 67:208\u2013229","journal-title":"Inf Fusion"},{"doi-asserted-by":"crossref","unstructured":"Agnihotri A, Kohli N (2023) A Hybrid Deep Neural approach for multi-class Classification of novel Corona Virus (COVID-19) using X-ray images. 2023 International conference on advancement in computation and computer technologies (InCACCT). IEEE","key":"16983_CR35","DOI":"10.1109\/InCACCT57535.2023.10141782"},{"issue":"10","key":"16983_CR36","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.3390\/diagnostics13101675","volume":"13","author":"IA Alablani","year":"2023","unstructured":"Alablani IA, Alenazi MJ (2023) COVID-ConvNet: a convolutional neural network classifier for diagnosing COVID-19 infection. Diagnostics 13(10):1675","journal-title":"Diagnostics"},{"doi-asserted-by":"crossref","unstructured":"Rattanawin P, Pakinsee T, Songmuang P (2023) A GoogLeNet performance approach for COVID-19 detection using chest X-ray images. In: 2023 15th international conference on knowledge and smart technology (KST). IEEE","key":"16983_CR37","DOI":"10.1109\/KST57286.2023.10086817"},{"issue":"1","key":"16983_CR38","doi-asserted-by":"publisher","first-page":"2181917","DOI":"10.1080\/23311916.2023.2181917","volume":"10","author":"MM Alghamdi","year":"2023","unstructured":"Alghamdi MM, Meshref MY, Dahab H, Alazwary NHA (2023) Enhancing deep learning techniques for the diagnosis of the novel coronavirus (COVID-19) using X-ray images. Cogent Eng 10(1):2181917","journal-title":"Cogent Eng"},{"doi-asserted-by":"crossref","unstructured":"Kirar BS et al (2023) Detection of COVID-19-affected persons using convolutional neural network from x-rays\u2019 images. Machine intelligence techniques for data analysis and signal processing: proceedings of the 4th international conference MISP 2022. Springer Nature, Singapore, vol 1","key":"16983_CR39","DOI":"10.1007\/978-981-99-0085-5_60"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16983-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16983-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16983-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T13:18:22Z","timestamp":1712236702000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16983-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,10]]},"references-count":39,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["16983"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16983-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,10,10]]},"assertion":[{"value":"29 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2023","order":4,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}