{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:15:42Z","timestamp":1732040142330},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11548-020-02305-w","type":"journal-article","created":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T07:03:33Z","timestamp":1610175813000},"page":"197-206","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network"],"prefix":"10.1007","volume":"16","author":[{"given":"Xiao","family":"Qi","sequence":"first","affiliation":[]},{"given":"Lloyd G.","family":"Brown","sequence":"additional","affiliation":[]},{"given":"David J.","family":"Foran","sequence":"additional","affiliation":[]},{"given":"John","family":"Nosher","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3232-8193","authenticated-orcid":false,"given":"Ilker","family":"Hacihaliloglu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,9]]},"reference":[{"key":"2305_CR1","first-page":"1","volume":"5","author":"T Singhal","year":"2020","unstructured":"Singhal T (2020) A review of coronavirus disease-2019 (covid-19). Indian J Pediatr 5:1\u20136","journal-title":"Indian J Pediatr"},{"key":"2305_CR2","first-page":"200490","volume":"3","author":"ZY Zu","year":"2020","unstructured":"Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ (2020) Coronavirus disease 2019 (covid-19): a perspective from china. Radiology 3:200490","journal-title":"Radiology"},{"issue":"5","key":"2305_CR3","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","volume":"20","author":"E Dong","year":"2020","unstructured":"Dong E, Du H, Gardner L (2020) An interactive web-based dashboard to track covid-19 in real time. Lancet Infect Dis 20(5):533\u2013534","journal-title":"Lancet Infect Dis"},{"issue":"18","key":"2305_CR4","first-page":"1843","volume":"323","author":"W Wang","year":"2020","unstructured":"Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, Tan W (2020) Detection of sars-cov-2 in different types of clinical specimens. JAMA 323(18):1843\u20131844","journal-title":"JAMA"},{"issue":"7","key":"2305_CR5","doi-asserted-by":"publisher","first-page":"4116","DOI":"10.1128\/AEM.69.7.4116-4122.2003","volume":"69","author":"G Bleve","year":"2003","unstructured":"Bleve G, Rizzotti L, Dellaglio F, Torriani S (2003) Development of reverse transcription (rt)-pcr and real-time rt-pcr assays for rapid detection and quantification of viable yeasts and molds contaminating yogurts and pasteurized food products. Appl Environ Microbiol 69(7):4116\u20134122","journal-title":"Appl Environ Microbiol"},{"key":"2305_CR6","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"2","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of covid-19 cases using deep neural networks with x-ray images. Comput Biol Med 2:103792","journal-title":"Comput Biol Med"},{"key":"2305_CR7","doi-asserted-by":"crossref","unstructured":"Wang L, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. arXiv preprint arXiv:2003.09871","DOI":"10.1038\/s41598-020-76550-z"},{"key":"2305_CR8","unstructured":"Farooq M, Hafeez A (2020) Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003.14395"},{"key":"2305_CR9","doi-asserted-by":"publisher","first-page":"109761","DOI":"10.1016\/j.mehy.2020.109761","volume":"3","author":"F Ucar","year":"2020","unstructured":"Ucar F, Korkmaz D (2020) Covidiagnosis-net: Deep bayes-squeezenet based diagnostic of the coronavirus disease 2019 (covid-19) from x-ray images. Med Hypotheses 3:109761","journal-title":"Med Hypotheses"},{"key":"2305_CR10","unstructured":"Siddhartha M, Santra A (2020) Covidlite: a depth-wise separable deep neural network with white balance and clahe for detection of covid-19. arXiv preprint arXiv:2006.13873"},{"key":"2305_CR11","unstructured":"Gour M, Jain S (2020) Stacked convolutional neural network for diagnosis of covid-19 disease from x-ray images. arXiv preprint arXiv:2006.13817"},{"key":"2305_CR12","unstructured":"Haghanifar A, Majdabadi MM, Ko S (2020) Covid-cxnet: Detecting covid-19 in frontal chest x-ray images using deep learning . https:\/\/github.com\/armiro\/COVID-CXNet"},{"key":"2305_CR13","first-page":"1","volume":"6","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 6:1","journal-title":"Phys Eng Sci Med"},{"key":"2305_CR14","unstructured":"Gonz\u00e1lez G, Bustos A, Salinas JM, de la Iglesia-Vaya M, Galant J, Cano-Espinosa C, Barber X, Orozco-Beltr\u00e1n D, Cazorla M, Pertusa, A (2020) Umls-chestnet: a deep convolutional neural network for radiological findings, differential diagnoses and localizations of covid-19 in chest x-rays. arXiv preprint arXiv:2006.05274"},{"key":"2305_CR15","doi-asserted-by":"crossref","unstructured":"Hacihaliloglu I (2017) Localization of bone surfaces from ultrasound data using local phase information and signal transmission maps. In: International workshop and challenge on computational methods and clinical applications in musculoskeletal imaging, pp 1\u201311. Springer","DOI":"10.1007\/978-3-319-74113-0_1"},{"issue":"12","key":"2305_CR16","doi-asserted-by":"publisher","first-page":"3136","DOI":"10.1109\/78.969520","volume":"49","author":"M Felsberg","year":"2001","unstructured":"Felsberg M, Sommer G (2001) The monogenic signal. IEEE Trans Signal Process 49(12):3136\u20133144","journal-title":"IEEE Trans Signal Process"},{"key":"2305_CR17","doi-asserted-by":"crossref","unstructured":"Belaid A, Boukerroui D (2014) $$\\alpha $$ scale spaces filters for phase based edge detection in ultrasound images. In: 2014 IEEE ISBI, pp 1247\u20131250. IEEE","DOI":"10.1109\/ISBI.2014.6868102"},{"key":"2305_CR18","doi-asserted-by":"crossref","unstructured":"Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE ICCV, pp 617\u2013624","DOI":"10.1109\/ICCV.2013.82"},{"key":"2305_CR19","unstructured":"Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: ICML"},{"key":"2305_CR20","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","volume":"3","author":"T Zhou","year":"2019","unstructured":"Zhou T, Ruan S, Canu S (2019) A review: deep learning for medical image segmentation using multi-modality fusion. Array 3:100004","journal-title":"Array"},{"issue":"5","key":"2305_CR21","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s11548-019-01934-0","volume":"14","author":"AZ Alsinan","year":"2019","unstructured":"Alsinan AZ, Patel VM, Hacihaliloglu I (2019) Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided cnn. Int J Comput Assist Radiol Surg 14(5):775\u2013783","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2305_CR22","unstructured":"Ayg\u00fcn M, \u015eahin YH, \u00dcnal G (2018) Multi modal convolutional neural networks for brain tumor segmentation. arXiv preprint arXiv:1809.06191"},{"key":"2305_CR23","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"2305_CR24","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016)Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"2305_CR25","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1109\/TMI.2017.2712367","volume":"36","author":"CF Baumgartner","year":"2017","unstructured":"Baumgartner CF, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch LM, Kainz B, Rueckert D (2017) Sononet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans Med Imaging 36(11):2204\u20132215","journal-title":"IEEE Trans Med Imaging"},{"key":"2305_CR26","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"2305_CR27","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-resnet and the impact of residual connections on learning","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"2305_CR28","unstructured":"Tan M, Le QV (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946"},{"key":"2305_CR29","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"2305_CR30","unstructured":"Ha Q, Liu B, Liu F (2020)Identifying melanoma images using efficientnet ensemble: Winning solution to the siim-isic melanoma classification challenge. arXiv preprint arXiv:2010.05351"},{"key":"2305_CR31","unstructured":"Kassani SH, Kassasni PH, Wesolowski MJ, Schneider KA, Deters R (2020) Automatic detection of coronavirus disease (covid-19) in x-ray and ct images: a machine learning-based approach. arXiv preprint arXiv:2004.10641"},{"key":"2305_CR32","unstructured":"de\u00a0la Iglesia\u00a0Vay\u00e1 M, Saborit JM, Montell JA, Pertusa A, Bustos A, Cazorla M, Galant J, Barber X, Orozco-Beltr\u00e1n D, Garc\u00eda-Garc\u00eda F, Caparr\u00f3s M, Gonz\u00e1lez, G, Salinas JM (2020) Bimcv covid-19+: a large annotated dataset of rx and ct images from covid-19 patients. arXiv preprint arXiv:2006.01174"},{"issue":"2","key":"2305_CR33","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2019","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2019) Grad-cam: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128(2):336\u2013359","journal-title":"Int J Comput Vis"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02305-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11548-020-02305-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02305-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T02:15:33Z","timestamp":1670724933000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11548-020-02305-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,9]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["2305"],"URL":"https:\/\/doi.org\/10.1007\/s11548-020-02305-w","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,9]]},"assertion":[{"value":"20 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The article uses open-source datasets.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Nothing to declare.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}