{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T09:30:18Z","timestamp":1769938218882,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s11042-021-10769-4","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T14:06:45Z","timestamp":1615903605000},"page":"21339-21361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["DPV: a taxonomy for utilizing deep learning as a prediction technique for various types of cancers detection"],"prefix":"10.1007","volume":"80","author":[{"given":"Bhagyashree","family":"Shah","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-3540","authenticated-orcid":false,"given":"Abeer","family":"Alsadoon","sequence":"additional","affiliation":[]},{"given":"P.W.C.","family":"Prasad","sequence":"additional","affiliation":[]},{"given":"Ghazi","family":"Al-Naymat","sequence":"additional","affiliation":[]},{"given":"Azam","family":"Beg","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,16]]},"reference":[{"issue":"1","key":"10769_CR1","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1021\/acs.jproteome.7b00595","volume":"17","author":"FM Alakwaa","year":"2017","unstructured":"Alakwaa FM, Chaudhary K, Garmire LX (2017) Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. J Proteome Res 17(1):337\u2013347. https:\/\/doi.org\/10.1021\/acs.jproteome.7b00595","journal-title":"J Proteome Res"},{"issue":"5","key":"10769_CR2","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1109\/TMI.2016.2528120","volume":"35","author":"S Albarqouni","year":"2016","unstructured":"Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N (2016) Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 35(5):1313\u20131321","journal-title":"IEEE Trans Med Imaging"},{"key":"10769_CR3","doi-asserted-by":"publisher","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","volume":"11","author":"S Anders","year":"2010","unstructured":"Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106. https:\/\/doi.org\/10.1186\/gb-2010-11-10-r106","journal-title":"Genome Biol"},{"issue":"4","key":"10769_CR4","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1002\/(SICI)1097-0142(19970215)79:4<857::AID-CNCR24>3.0.CO;2-Y","volume":"79","author":"H Burkey","year":"2007","unstructured":"Burkey H, Goodman P, Rosen D, Henson D, Weinstein J, Harrell F, Marks J, Winchester D, Bostwick D (2007) Artificial neural networks improve the accuracy of cancer survival prediction. Cancer. 79(4):857\u2013862","journal-title":"Cancer."},{"issue":"1","key":"10769_CR5","doi-asserted-by":"publisher","first-page":"3395","DOI":"10.1038\/s41598-018-21758-3","volume":"8","author":"D Bychkov","year":"2018","unstructured":"Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, Walliander M, Lundin M, Haglund C, Lundin J (2018) Deep learning-based tissue analysis predicts outcome in colorectal cancer. Sci Rep 8(1):3395. https:\/\/doi.org\/10.1038\/s41598-018-21758-3","journal-title":"Sci Rep"},{"key":"10769_CR6","doi-asserted-by":"publisher","unstructured":"Chen D, Qian G, Shi C, Pan Q (2017) Breast cancer malignancy prediction using incremental combination of multiple recurrent neural networks. In international conference on neural information processing, 10635, 43\u201352. https:\/\/doi.org\/10.1007\/978-3-319-70096-0_5","DOI":"10.1007\/978-3-319-70096-0_5"},{"key":"10769_CR7","doi-asserted-by":"publisher","unstructured":"Choi H, Na KJ (2018) A risk stratification model for lung Cancer based on gene Coexpression network and deep learning. BioMed research international, 2018, 11, doi: https:\/\/doi.org\/10.1155\/2018\/2914280","DOI":"10.1155\/2018\/2914280"},{"key":"10769_CR8","doi-asserted-by":"publisher","first-page":"46450","DOI":"10.1038\/srep46450","volume":"7","author":"A Cruz-Roa","year":"2017","unstructured":"Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NN, \u2026 Madabhushi A (2017) Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci Rep 7:46450. https:\/\/doi.org\/10.1038\/srep46450","journal-title":"Sci Rep"},{"key":"10769_CR9","doi-asserted-by":"publisher","unstructured":"Echaniz O, Gra\u00f1a M (2017) Ongoing work on deep learning for lung Cancer prediction. In international work-conference on the interplay between natural and artificial computation (pp. 42-48). Springer, Cham.Doi : https:\/\/doi.org\/10.1007\/978-3-319-59773-7","DOI":"10.1007\/978-3-319-59773-7"},{"issue":"7639","key":"10769_CR10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks [published correction appears in nature. 2017 Jun 28;546(7660):686]. Nature. 542(7639):115\u2013118. https:\/\/doi.org\/10.1038\/nature21056","journal-title":"Nature."},{"key":"10769_CR11","doi-asserted-by":"publisher","first-page":"e154","DOI":"10.7717\/peerj-cs.154","volume":"4","author":"K Fernandes","year":"2018","unstructured":"Fernandes K, Chicco D, Cardoso JS, Fernandes J (2018) Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. PeerJ Computer Science 4:e154. http:\/\/dx.doi.org.ezproxy.csu.edu.au\/10.7717\/peerj-cs.154","journal-title":"PeerJ Computer Science"},{"issue":"4","key":"10769_CR12","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s12918-018-0572-z","volume":"12","author":"M Fu","year":"2018","unstructured":"Fu M, Wu W, Hong X, Liu Q, Jiang J, Ou Y, Zhao Y, Gong X (2018) Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. BMC Syst Biol 12(4):56. https:\/\/doi.org\/10.1186\/s12918-018-0572-z","journal-title":"BMC Syst Biol"},{"key":"10769_CR13","doi-asserted-by":"publisher","unstructured":"Hareendran SA, Vinod Chandra SS, Prasad SR, Dhanya S (2020) Deep Learning Strategies for Survival Prediction in Prophylactic Resection Patients. In: Tan Y., Shi Y., Tuba M. (eds) Advances in Swarm Intelligence. ICSI 2020. Lecture notes in computer science, vol 12145. Springer, Cham https:\/\/doi.org\/10.1007\/978-3-030-53956-6_53","DOI":"10.1007\/978-3-030-53956-6_53"},{"issue":"6","key":"10769_CR14","doi-asserted-by":"publisher","first-page":"AB434","DOI":"10.1016\/j.gie.2018.04.1946","volume":"87","author":"Y Hashimoto","year":"2018","unstructured":"Hashimoto Y, Ohno I, Imaoka H, Takahashi H, Mitsunaga S, Sasaki M, \u2026 Kan M (2018) Mo1296 RELIMINARY RESULT OF COMPUTER AIDED DIAGNOSIS (CAD) PERFORMANCE USING DEEP LEARNING IN EUS-FNA CYTOLOGY OF PANCREATIC CANCER. Gastrointest Endosc 87(6):AB434 Retrieved from: https:\/\/www.giejournal.org\/article\/S0016-5107(18)32221-1\/pdf","journal-title":"Gastrointest Endosc"},{"key":"10769_CR15","doi-asserted-by":"crossref","unstructured":"Islam MM, Ajwad R, Chi C, Domaratzki M, Wang Y, Hu P (2017) Somatic copy number alteration-based prediction of molecular subtypes of breast Cancer using deep learning model. In Canadian conference on artificial intelligence (pp. 57-63). Springer, Cham. Retrieved from:https:\/\/www.tib.eu\/en\/search\/id\/TIBKAT%3A887495818\/","DOI":"10.1007\/978-3-319-57351-9_7"},{"issue":"3","key":"10769_CR16","doi-asserted-by":"publisher","first-page":"304","DOI":"10.21037\/tlcr.2018.05.15","volume":"7","author":"T Kadir","year":"2018","unstructured":"Kadir T, Gleeson F (2018) Lung cancer prediction using machine learning and advanced imaging techniques. Trans Lung Cancer res 7(3):304\u2013312. https:\/\/doi.org\/10.21037\/tlcr.2018.05.15","journal-title":"Trans Lung Cancer res"},{"issue":"6","key":"10769_CR17","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/TBME.2014.2303294","volume":"61","author":"A Khan","year":"2014","unstructured":"Khan A, Rajpoot N, Treanor D, Magee D (2014) A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color Deconvolution. IEEE Trans Biomed Eng 61(6):1729\u20131738. https:\/\/doi.org\/10.1109\/TBME.2014.2303294","journal-title":"IEEE Trans Biomed Eng"},{"issue":"6","key":"10769_CR18","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1073\/pnas.1717960115","volume":"115","author":"BJ Kim","year":"2018","unstructured":"Kim BJ, Kim SH (2018) Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method. Proc Natl Acad Sci U S A 115(6):1322\u20131327. https:\/\/doi.org\/10.1073\/pnas.1717960115","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"2","key":"10769_CR19","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s12920-018-0349-7","volume":"11","author":"S Kim","year":"2018","unstructured":"Kim S, Lee H, Kim K, Kang J (2018) Mut2Vec: distributed representation of cancerous mutations. BMC Med Genet 11(2):33. https:\/\/doi.org\/10.1186\/s12920-018-0349-7","journal-title":"BMC Med Genet"},{"key":"10769_CR20","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","volume":"35","author":"T Kooi","year":"2017","unstructured":"Kooi T, Litjens G, van Ginneken B, Gubern-M\u00e9rida A, S\u00e1nchez CI, Mann R, den Heeten A, Karssemeijer N (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303\u2013312. https:\/\/doi.org\/10.1016\/j.media.2016.07.007","journal-title":"Med Image Anal"},{"issue":"2","key":"10769_CR21","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1164\/rccm.201709-1879ED","volume":"197","author":"WW Labaki","year":"2018","unstructured":"Labaki WW, Han MK (2018) (2018). Artificial intelligence and chest imaging. Will deep learning make us smarter? Am J Respir Crit Care Med 197(2):148\u2013150. https:\/\/doi.org\/10.1164\/rccm.201709-1879ED","journal-title":"Am J Respir Crit Care Med"},{"issue":"6","key":"10769_CR22","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/j.ajog.2017.08.012","volume":"217","author":"K Matsuo","year":"2017","unstructured":"Matsuo K, Purushotham S, Moeini A, Li G, Machida H, Liu Y, Roman LD (2017) A pilot study in using deep learning to predict limited life expectancy in women with recurrent cervical cancer. Am J Obstetrics Gynecol 217(6):703\u2013705. https:\/\/doi.org\/10.1016\/j.ajog.2017.08.012","journal-title":"Am J Obstetrics Gynecol"},{"key":"10769_CR23","doi-asserted-by":"publisher","unstructured":"Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Vega JEV ... Cooper LA (2018) Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences, 201717139. 115(13), E2970-E2979 Retrieved from: https:\/\/doi.org\/10.1073\/pnas.1717139115","DOI":"10.1073\/pnas.1717139115"},{"issue":"9","key":"10769_CR24","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","volume":"34","author":"K Preuer","year":"2017","unstructured":"Preuer K, Lewis RP, Hochreiter S, Bender A, Bulusu KC, Klambauer G (2017) DeepSynergy: predicting anti-cancer drug synergy with deep learning. Bioinformatics 34(9):1538\u20131546. https:\/\/doi.org\/10.1093\/bioinformatics\/btx806","journal-title":"Bioinformatics"},{"key":"10769_CR25","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.compmedimag.2017.06.001","volume":"61","author":"H Sharma","year":"2017","unstructured":"Sharma H, Zerbe N, Klempert I, Hellwich O, Hufnagl P (2017) Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph 61:2\u201313. https:\/\/doi.org\/10.1016\/j.compmedimag.2017.06.001","journal-title":"Comput Med Imaging Graph"},{"issue":"5","key":"10769_CR26","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana K, Raza SEA, Tsang YW, Snead DR, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35(5):1196\u20131206. https:\/\/doi.org\/10.1109\/TMI.2016.2525803","journal-title":"IEEE Trans Med Imaging"},{"key":"10769_CR27","doi-asserted-by":"publisher","unstructured":"Song Y, Zhang Yu, Yan, X, Liu, H, Zhou, M, Hu, B, Yang, G (2018) Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. 48(6):1570-1577. Doi: https:\/\/doi.org\/10.1002\/jmri.26047","DOI":"10.1002\/jmri.26047"},{"issue":"3","key":"10769_CR28","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TCBB.2018.2806438","volume":"16","author":"D Sun","year":"2018","unstructured":"Sun D, Wang M, Li A (2018) A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE\/ACM Trans Comput Biol Bioinform 16(3):841\u2013850. https:\/\/doi.org\/10.1109\/TCBB.2018.2806438","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"10769_CR29","doi-asserted-by":"publisher","first-page":"17256","DOI":"10.1038\/s41598-019-53989-3","volume":"9","author":"Y Sun","year":"2019","unstructured":"Sun Y, Zhu S, Ma K, Liu W, Yue Y, Hu G, Lu H, Chen W (2019) Identification of 12 cancer types through genome deep learning. Sci Rep 9:17256. https:\/\/doi.org\/10.1038\/s41598-019-53989-3","journal-title":"Sci Rep"},{"key":"10769_CR30","doi-asserted-by":"publisher","unstructured":"Urban G, Bache KM, Phan D, Sobrino A, Shmakov AK, Hachey SJ, Baldi P (2018) Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularisation Images. IEEE\/ACM Transactions on Computational Biology and Bioinformatics. vol. 16, no. 3, pp. 1029\u20131035, 1 May\u2013June 2019 DOI: https:\/\/doi.org\/10.1109\/TCBB.2018.2841396","DOI":"10.1109\/TCBB.2018.2841396"},{"issue":"14","key":"10769_CR31","doi-asserted-by":"publisher","first-page":"3922","DOI":"10.1158\/0008-5472.CAN-17-0122","volume":"77","author":"ER Velazquez","year":"2017","unstructured":"Velazquez ER, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, \u2026 Gillies R (2017) Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 77(14):3922\u20133930. https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0122","journal-title":"Cancer Res"},{"key":"10769_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2017.09.005","volume":"153","author":"Y Xiao","year":"2018","unstructured":"Xiao Y, Wu J, Lin Z, Zhao X (2018) A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Prog Biomed 153:1\u20139. https:\/\/doi.org\/10.1016\/j.cmpb.2017.09.005","journal-title":"Comput Methods Prog Biomed"},{"issue":"1","key":"10769_CR33","doi-asserted-by":"publisher","first-page":"11707","DOI":"10.1038\/s41598-017-11817-6","volume":"7","author":"S Yousefi","year":"2017","unstructured":"Yousefi S, Amrollahi F, Amgad M, Dong C, Lewis JE, Song C, Gutman DA, Halani SH, Velazquez Vega JE, Brat DJ, Cooper LA (2017) Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci Rep 7(1):11707. https:\/\/doi.org\/10.1038\/s41598-017-11817-6","journal-title":"Sci Rep"},{"key":"10769_CR34","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.patcog.2017.12.017","volume":"77","author":"X Yuan","year":"2018","unstructured":"Yuan X, Xie L, Abouelenien M (2018) A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data. Pattern Recogn 77:160\u2013172","journal-title":"Pattern Recogn"},{"issue":"3","key":"10769_CR35","doi-asserted-by":"publisher","first-page":"603","DOI":"10.3390\/cancers12030603","volume":"12","author":"W Zhu","year":"2020","unstructured":"Zhu W, Xie L, Han J, Guo X (2020) The application of deep learning in Cancer prognosis prediction. Cancers 12(3):603. https:\/\/doi.org\/10.3390\/cancers12030603","journal-title":"Cancers"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10769-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10769-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10769-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T05:15:09Z","timestamp":1621919709000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10769-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,16]]},"references-count":35,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["10769"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10769-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,16]]},"assertion":[{"value":"24 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}