{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:23:55Z","timestamp":1778603035500,"version":"3.51.4"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Gyeonggi-do Regional Research Center (GRRC) program of Gyeonggi province","award":["[GRRC-Gachon2017(B01), Analysis of behavior based on senior life log]"],"award-info":[{"award-number":["[GRRC-Gachon2017(B01), Analysis of behavior based on senior life log]"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Korea government","doi-asserted-by":"publisher","award":["2020R1A2C1011708"],"award-info":[{"award-number":["2020R1A2C1011708"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2996770","type":"journal-article","created":{"date-parts":[[2020,5,22]],"date-time":"2020-05-22T20:32:50Z","timestamp":1590179570000},"page":"99227-99238","source":"Crossref","is-referenced-by-count":83,"title":["Colorectal Disease Classification Using Efficiently Scaled Dilation in Convolutional Neural Network"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6574-038X","authenticated-orcid":false,"given":"Sahadev","family":"Poudel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoon Jae","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duc My","family":"Vo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8117-6566","authenticated-orcid":false,"given":"Sang-Woong","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx651"},{"key":"ref38","first-page":"97","article-title":"A supervised learning based approach to detect Crohn’s disease in abdominal MR volumes","author":"mahapatra","year":"2012","journal-title":"Proc Int MICCAI Workshop Comput Clin Challenges Abdominal Imag"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2018.06.037"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98446-9_44"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.3.034504"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3149572.3149613"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/6584725"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2017.8037556"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2018.10.020"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.3963"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-016-1508-7"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2013.6556432"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-02606-2"},{"key":"ref2","author":"braithwaite","year":"2016","journal-title":"American Cancer Society Cancer Facts and Figures"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.4103\/jpi.jpi_34_17"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7899954"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.02.001"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2011.05.006"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2014.6943274"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2012.08.003"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9576-9"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1117\/12.808103"},{"key":"ref10","first-page":"85","article-title":"Tumor recognition in wireless capsule endoscopy images","volume":"15","author":"sawant","year":"2015","journal-title":"Int J Comput Sci Netw Secur"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3083187.3083212"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref12","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2973390"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535302"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4989-y"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2635662"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref18","first-page":"10727","article-title":"DropBlock: A regularization method for convolutional networks","author":"ghiasi","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.08.007"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.2478\/v10136-012-0031-x"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/jcm8040462"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-25842-6"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.12.089"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2801119"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-018-0793-2"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TITB.2012.2185807","article-title":"Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection","volume":"16","author":"li","year":"2012","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref48","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.75"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2018.09.024"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref43","first-page":"4278","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09098911.pdf?arnumber=9098911","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:52:15Z","timestamp":1639770735000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9098911\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2996770","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}