{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:14:10Z","timestamp":1783937650880,"version":"3.55.0"},"reference-count":84,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Tumori"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Comprehensive identification and prioritization of developed artificial intelligence methods for the detection and diagnosis of breast cancer can help to select proper techniques. This study aimed to introduce the best artificial intelligence techniques developed for the detection and diagnosis of breast cancer using microscopic images by fuzzy AHP-TOPSIS techniques.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>To identify the artificial intelligence techniques developed, a systematic search was performed in five reliable databases. After that, the Delphi method was applied to determine the proper criteria for selecting the best artificial intelligence techniques. To estimate the relative weights of the criteria, the fuzzy analytical hierarchy process (FAHP) method was used. In the next step, to prioritize the identified artificial intelligence techniques, the technique for order of preference by similarity to the ideal solution (TOPSIS) method was applied.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Forty-four artificial intelligence techniques were identified. Seven selection criteria, validity, accuracy, comprehensiveness, processing time, cost, simplicity, and executive capability, were introduced. Ensemble deep learning architectures integrated with web of things (weight = 0.8041), the computer-aided diagnosis method (weight = 0.7774), the ensemble strategy (VGG16 - ResNet34 - ResNet50) (weight = 0.7475), automated tumor-stroma interface zone detection (weight = 0.7475), and MultiNet\/Computer-Aided Diagnosis (CAD)-based deep learning model (weight = 0.7262) were selected as the best methods, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>These findings represent a total approach to the developed techniques which can be used for designing methods with better performance in the future.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03008916261434125","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:13:01Z","timestamp":1776402781000},"page":"241-259","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification and selection of the best artificial intelligence methods developed for detection and diagnosis of breast cancer"],"prefix":"10.1177","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2087-9242","authenticated-orcid":false,"given":"Xuan","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, Affiliated Hospital of Traditional Chinese Medicine Xinjiang Medical University, Urumqi, Xinjiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9472-4876","authenticated-orcid":false,"given":"Qi","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Dongtai People's Hospital, Dongtai, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0543-4280","authenticated-orcid":false,"given":"Dan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, Qian Wei Hospital of Jilin Province, ChangChun, JiLin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6013-7692","authenticated-orcid":false,"given":"Jing","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Affiliated Hospital of Traditional Chinese Medicine Xinjiang Medical University, Urumqi, Xinjiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9220-3279","authenticated-orcid":false,"given":"Lijun","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Radiology, The People\u2019s Hospital of Zhaoyuan City, Zhaoyuan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2024.172556"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/10807039.2023.2167193"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00864"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1200\/JGO.17.00207"},{"key":"e_1_3_3_8_2","article-title":"MultiNet: A deep neural network approach for detecting breast cancer through multi-scale feature fusion","volume":"34","author":"Khan SI","year":"2022","unstructured":"Khan SI, Shahrior A, Karim R, et al. MultiNet: A deep neural network approach for detecting breast cancer through multi-scale feature fusion. J King Saud Uni Computer Info Sci 2022; 34: 6217-228.","journal-title":"J King Saud Uni Computer Info Sci"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2015.01.1150"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2015.05.017"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-112415-114722"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01190"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114095"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104048"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.34855"},{"key":"e_1_3_3_16_2","first-page":"258","article-title":"Enhanced feature selection for imbalanced microarray cancer gene classification using chaotic salp swarm algorithm","volume":"2025","author":"Aliyu BS","unstructured":"Aliyu BS, Isuwa J, Abdulrahim A, et al. Enhanced feature selection for imbalanced microarray cancer gene classification using chaotic salp swarm algorithm. Int J Theor App Comput Intell 2025; 258-283.","journal-title":"Int J Theor App Comput Intell"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-020-00403-x"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-016-2131-6"},{"key":"e_1_3_3_19_2","first-page":"417","article-title":"Breast cancer detection in mammogram images using deep learning technique","volume":"25","author":"Selvathi D","year":"2017","unstructured":"Selvathi D, Poornila AA. Breast cancer detection in mammogram images using deep learning technique. Middle-East J Sci Res 2017; 25: 417-426.","journal-title":"Middle-East J Sci Res"},{"key":"e_1_3_3_20_2","first-page":"145","article-title":"Detection and classification of skin cancer by using CNN-enabled cloud storage data access control algorithm based on blockchain technology","author":"Nasir S","year":"2025","unstructured":"Nasir S, Bilal M, Khalidi H. Detection and classification of skin cancer by using CNN-enabled cloud storage data access control algorithm based on blockchain technology. Int J Theor App Comput Intell 2025; 145-169.","journal-title":"Int J Theor App Comput Intell"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22961"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123747"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23222"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-018-4638-5"},{"key":"e_1_3_3_25_2","first-page":"181","article-title":"Feature selection methods in big medical databases: A comprehensive survey","volume":"2025","author":"Karimi M","unstructured":"Karimi M, Karimi Z, Khosravi M, et al. Feature selection methods in big medical databases: A comprehensive survey. Int J Theor App Comp Intell 2025; 181-209.","journal-title":"Int J Theor App Comp Intell"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102276"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.2174\/1573405619666230126093806"},{"key":"e_1_3_3_28_2","first-page":"219","article-title":"Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review","volume":"2018","author":"Sadoughi F","unstructured":"Sadoughi F, Kazemy Z, Hamedan F, et al. Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review. Breast Cancer Target Ther 2018; 1: 219-230.","journal-title":"Breast Cancer Target Ther"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13010045"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2017.04.041"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-023-16969-x"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ECCE64574.2025.11013439"},{"key":"e_1_3_3_33_2","first-page":"304","article-title":"Decision-making in selecting diagnostic methods for early-stage cancer using hybrid MCDM-fuzzy approach","volume":"8","author":"Jana P","year":"2025","unstructured":"Jana P, Kundu P, Jana P. Decision-making in selecting diagnostic methods for early-stage cancer using hybrid MCDM-fuzzy approach. Int J 2025; 8: 304.","journal-title":"Int J"},{"key":"e_1_3_3_34_2","doi-asserted-by":"crossref","unstructured":"Gupta A Kumar R Kumar R et al. A post-benchmarking framework towards evaluating and ranking deep learning models for breast cancer detection using hybrid MCDM. 2024 International Conference on Network Security and Blockchain Technology Singapore; 2024: Springer.","DOI":"10.1007\/978-981-97-8051-8_24"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2"},{"key":"e_1_3_3_36_2","volume-title":"The Delphi Technique in Nursing and Health Research","author":"Keeney S","year":"2010","unstructured":"Keeney S, McKenna HP, Hasson F. The Delphi Technique in Nursing and Health Research. John Wiley & Sons; 2010."},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.08.019"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.05.056"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.14306\/renhyd.18.3.114"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.smrv.2023.101846"},{"key":"e_1_3_3_41_2","unstructured":"Dlikilili X van Rooyen J. An application of a two-step Delphi model in identification of factors affecting the competitive performance of the South African citrus industry. Agriculture Economics Association of South Africa Annual Conference Cape Town South Africa; 2018: 284731."},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.3397\/1\/376738"},{"key":"e_1_3_3_43_2","article-title":"Association between fatigue and occupational physical trauma among male Iranian workers in the copper extraction industry","volume":"22","author":"Khoshakhlagh AH","year":"2017","unstructured":"Khoshakhlagh AH, Ghasemi M, Pourtaghi G. Association between fatigue and occupational physical trauma among male Iranian workers in the copper extraction industry. Trauma Month 2017; 22:1-10.","journal-title":"Trauma Month"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajpath.2020.01.018"},{"key":"e_1_3_3_45_2","volume-title":"2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","author":"VN SK","year":"2021","unstructured":"VN SK, Vijay M. Diagnosing Cancer Cells Using Histopathological Images with Deep Learning. 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India; 2021: IEEE."},{"key":"e_1_3_3_46_2","first-page":"19","article-title":"Nuclei segmentation for computer-aided diagnosis of breast cancer. International","volume":"24","author":"Kowal M","year":"2014","unstructured":"Kowal M, Filipczuk P. Nuclei segmentation for computer-aided diagnosis of breast cancer. International J App Math Comp Sci 2014; 24: 19-31.","journal-title":"J App Math Comp Sci"},{"key":"e_1_3_3_47_2","first-page":"1739","article-title":"A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection","volume":"34","author":"Kalita M","year":"2024","unstructured":"Kalita M, Mahanta LB, Das AK, et al. A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection. Indo J Elec Eng Comp Sci 2024; 34: 1739-1752.","journal-title":"Indo J Elec Eng Comp Sci"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.27.7.076003"},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/IEMTRONICS51293.2020.9216455"},{"key":"e_1_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/AICCSA.2018.8612799"},{"key":"e_1_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.3390\/biomedinformatics4020055"},{"key":"e_1_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2012.6235887"},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16122222"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRCICN.2018.8718736"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2013.08.003"},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1615\/CritRevBiomedEng.2022043417"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0320(19980901)33:1<32::AID-CYTO4>3.0.CO;2-D"},{"key":"e_1_3_3_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIUCC52538.2021.9447653"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1080\/03772063.2021.1962741"},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.1111\/jmi.12645"},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/SoCPaR.2011.6089105"},{"key":"e_1_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24653-4_26"},{"key":"e_1_3_3_63_2","article-title":"FastLeakyResNet-CIR: A novel deep learning framework for breast cancer detection and classification","author":"Zeng R","year":"2024","unstructured":"Zeng R, Qu B, Liu W, et al. FastLeakyResNet-CIR: A novel deep learning framework for breast cancer detection and classification. IEEE Access. 2024;12:70825-70832.","journal-title":"IEEE Access"},{"key":"e_1_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/SAI.2016.7555997"},{"key":"e_1_3_3_65_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0208385"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-31196-3_9"},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2275151"},{"key":"e_1_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.4048\/jbc.2010.13.4.349"},{"key":"e_1_3_3_69_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.911757"},{"key":"e_1_3_3_70_2","volume-title":"Recent Developments and Achievements in Biocybernetics and Biomedical Engineering: Proceedings of the 20th Polish Conference on Biocybernetics and Biomedical Engineering","volume":"20","author":"Les T","year":"2017","unstructured":"Les T, Markiewicz T, Patera J. Automatic Detection of Cells in FISH Images Using Map of Colors and Three-Track Segmentation. Recent Developments and Achievements in Biocybernetics and Biomedical Engineering: Proceedings of the 20th Polish Conference on Biocybernetics and Biomedical Engineering, Krak\u00f3w, Poland, September 20-22, 2017 20; 2018: Springer."},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2022.100147"},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66905-2_20"},{"key":"e_1_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2013.6611260"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.2217\/bmm.15.102"},{"key":"e_1_3_3_75_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10544-015-9995-0"},{"key":"e_1_3_3_76_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10544-015-9999-9"},{"key":"e_1_3_3_77_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10544-016-0103-x"},{"key":"e_1_3_3_78_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-014-0092-3"},{"key":"e_1_3_3_79_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-015-1361-0"},{"key":"e_1_3_3_80_2","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.3002564"},{"key":"e_1_3_3_81_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep00503"},{"key":"e_1_3_3_82_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1755\/1\/012026"},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.1093\/bjsopen\/zrad046"},{"key":"e_1_3_3_84_2","first-page":"5041","article-title":"Extraction of breast border and removal of pectoral muscle in wavelet domain","volume":"28","author":"Mughal B","year":"2017","unstructured":"Mughal B, Muhammad N, Sharif M, et al. Extraction of breast border and removal of pectoral muscle in wavelet domain. Biomed Res 2017; 28: 5041-5043.","journal-title":"Biomed Res"},{"key":"e_1_3_3_85_2","doi-asserted-by":"publisher","DOI":"10.1136\/svn-2017-000101"}],"container-title":["Tumori Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/03008916261434125","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/03008916261434125","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/03008916261434125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T09:29:07Z","timestamp":1783934947000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/03008916261434125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,16]]},"references-count":84,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["10.1177\/03008916261434125"],"URL":"https:\/\/doi.org\/10.1177\/03008916261434125","relation":{},"ISSN":["0300-8916","2038-2529"],"issn-type":[{"value":"0300-8916","type":"print"},{"value":"2038-2529","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,16]]}}}