{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:19:32Z","timestamp":1773800372881,"version":"3.50.1"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"vor","delay-in-days":61,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Lung cancer has been the leading cause of cancer death for many decades. With the advent of artificial intelligence, various machine learning models have been proposed for lung cancer detection (LCD). Typically, challenges in building an accurate LCD model are the small\u2010scale datasets, the poor generalizability to detect unseen data, and the selection of useful source domains and prioritization of multiple source domains for transfer learning. In this paper, a multiround transfer learning and modified generative adversarial network (MTL\u2010MGAN) algorithm is proposed for LCD. The MTL transfers the knowledge between the prioritized source domains and target domain to get rid of exhaust search of datasets prioritization among multiple datasets, maximizing the transferability with a multiround transfer learning process, and avoiding negative transfer via customization of loss functions in the aspects of domain, instance, and feature. In regard to the MGAN, it not only generates additional training data but also creates intermediate domains to bridge the gap between the source domains and target domains. 10 benchmark datasets are chosen for the performance evaluation and analysis of the MTL\u2010MGAN. The proposed algorithm has significantly improved the accuracy compared with related works. To examine the contributions of the individual components of the MTL\u2010MGAN, ablation studies are conducted to confirm the effectiveness of the prioritization algorithm, the MTL, the negative transfer avoidance via loss functions, and the MGAN. The research implications are to confirm the feasibility of multiround transfer learning to enhance the optimal solution of the target model and to provide a generic approach to bridge the gap between the source domain and target domain using MGAN.<\/jats:p>","DOI":"10.1155\/2023\/6376275","type":"journal-article","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T20:05:05Z","timestamp":1677873905000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7992-9901","authenticated-orcid":false,"given":"Kwok Tai","family":"Chui","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4929-4698","authenticated-orcid":false,"given":"Brij B.","family":"Gupta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3285-7346","authenticated-orcid":false,"given":"Rutvij H.","family":"Jhaveri","sequence":"additional","affiliation":[]},{"given":"Hao Ran","family":"Chi","sequence":"additional","affiliation":[]},{"given":"Varsha","family":"Arya","sequence":"additional","affiliation":[]},{"given":"Ammar","family":"Almomani","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Nauman","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"e_1_2_9_1_2","volume-title":"Global Cancer Observatory: Cancer Today","author":"Ferlay K.","year":"2020"},{"key":"e_1_2_9_2_2","volume-title":"Global Strategy on Human Resources for Health: Workforce 2030","author":"World Health Organization","year":"2016"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3453168"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2023.124299"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.healun.2022.09.009"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2009.191"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/jproc.2020.3004555"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3191669"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1142\/s0218488522500222"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14133174"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2021.3123572"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3895-1"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1108\/ijicc-07-2021-0147"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10061058"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-022-02694-0"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/36.101377"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2963742"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/jas.2022.106004"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3130191"},{"key":"e_1_2_9_21_2","unstructured":"OdenaA. OlahC. andShlensJ. Conditional image synthesis with auxiliary classifier GANs Proceedings of the 34th International Conference on Machine Learning August 2017 Sydney Australia JMLR 2642\u20132651."},{"key":"e_1_2_9_22_2","unstructured":"MirzaM.andOsinderoS. Conditional generative adversarial nets 2014 https:\/\/arxiv.org\/abs\/1411.1784."},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5006"},{"key":"e_1_2_9_24_2","first-page":"1","article-title":"Corrigendum: decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts H. J.","year":"2014","journal-title":"Nature Communications"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.2.2.020103"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0118261"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13141"},{"key":"e_1_2_9_28_2","volume-title":"Learning Multiple Layers of Features from Tiny Images","author":"Krizhevsky A.","year":"2019"},{"key":"e_1_2_9_29_2","unstructured":"DengJ. DongW. SocherR. LiL. J. LiK. andFei-FeiL. Imagenet: a large-scale hierarchical image database Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition June 2009 Miami FL USA IEEE 248\u2013255."},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"LinT. Y. MaireM. BelongieS. HaysJ. PeronaP. RamananD. andZitnickC. L. Microsoft coco: common objects in context Proceedings of the European Conference on Computer Vision September 2014 Zurich Switzerland Springer 740\u2013755.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.111.100883"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1097\/rli.0000000000000100"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14153687"},{"key":"e_1_2_9_34_2","first-page":"25","article-title":"A comparative study of nine machine learning techniques used for the prediction of diseases","volume":"16","author":"Azar D.","year":"2018","journal-title":"International Journal of Artificial Intelligence"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1080\/17517575.2021.2023764"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.3390\/su142113998"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2018.2812301"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsyst.2016.2550530"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2878681"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.4018\/ijswis.297032"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01241-0"},{"key":"e_1_2_9_42_2","doi-asserted-by":"crossref","unstructured":"GergesF. ShihF. andAzarD. Automated diagnosis of acne and rosacea using convolution neural networks Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition September 2021 Xiamen China 607\u2013613.","DOI":"10.1145\/3488933.3488993"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2021.3117407"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1515\/med-2020-0028"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01904-8"},{"key":"e_1_2_9_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10042-y"},{"key":"e_1_2_9_47_2","doi-asserted-by":"publisher","DOI":"10.3390\/math10030464"},{"key":"e_1_2_9_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09676-6"},{"key":"e_1_2_9_49_2","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12938"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/6376275.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/6376275.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2023\/6376275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:11:53Z","timestamp":1735621913000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2023\/6376275"}},"subtitle":[],"editor":[{"given":"Lianyong","family":"Qi","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1155\/2023\/6376275"],"URL":"https:\/\/doi.org\/10.1155\/2023\/6376275","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2022-10-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6376275"}}