{"id":"https://openalex.org/W3023402959","doi":"https://doi.org/10.1109/access.2020.2993536","title":"Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis","display_name":"Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3023402959","doi":"https://doi.org/10.1109/access.2020.2993536","mag":"3023402959"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2993536","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2993536","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09089849.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09089849.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041170845","display_name":"Jing Zheng","orcid":"https://orcid.org/0000-0003-2540-6077"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing Zheng","raw_affiliation_strings":["Shenzhen Center for Health Information, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-2540-6077","affiliations":[{"raw_affiliation_string":"Shenzhen Center for Health Information, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058818219","display_name":"Denan Lin","orcid":"https://orcid.org/0000-0002-0175-3229"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Denan Lin","raw_affiliation_strings":["Shenzhen Center for Health Information, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-0175-3229","affiliations":[{"raw_affiliation_string":"Shenzhen Center for Health Information, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018910156","display_name":"Zhongjun Gao","orcid":"https://orcid.org/0000-0003-2009-2777"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongjun Gao","raw_affiliation_strings":["Chengdu Gold Disk UESTC Multimedia Technology Company, Ltd., Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-2009-2777","affiliations":[{"raw_affiliation_string":"Chengdu Gold Disk UESTC Multimedia Technology Company, Ltd., Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100673319","display_name":"Shuang Wang","orcid":"https://orcid.org/0000-0003-4490-6368"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuang Wang","raw_affiliation_strings":["Shenzhen Center for Health Information, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-4490-6368","affiliations":[{"raw_affiliation_string":"Shenzhen Center for Health Information, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101758582","display_name":"Mingjie He","orcid":"https://orcid.org/0000-0003-2194-1021"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingjie He","raw_affiliation_strings":["Chengdu Gold Disk UESTC Multimedia Technology Company, Ltd., Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-2194-1021","affiliations":[{"raw_affiliation_string":"Chengdu Gold Disk UESTC Multimedia Technology Company, Ltd., Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021918863","display_name":"Jipeng Fan","orcid":"https://orcid.org/0000-0002-8276-384X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jipeng Fan","raw_affiliation_strings":["Chengdu Gold Disk UESTC Multimedia Technology Company, Ltd., Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-8276-384X","affiliations":[{"raw_affiliation_string":"Chengdu Gold Disk UESTC Multimedia Technology Company, Ltd., Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":20.6757,"has_fulltext":true,"cited_by_count":313,"citation_normalized_percentile":{"value":0.99517207,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"8","issue":null,"first_page":"96946","last_page":"96954"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.984499990940094,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.974399983882904,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.8098278045654297},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6788184642791748},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5925546884536743},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.5854213237762451},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44566407799720764},{"id":"https://openalex.org/keywords/cancer-detection","display_name":"Cancer detection","score":0.42058494687080383},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41009145975112915},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3917819857597351},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3750658333301544},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.32335737347602844},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.16349148750305176},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13142916560173035},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.08951348066329956}],"concepts":[{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.8098278045654297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6788184642791748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5925546884536743},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.5854213237762451},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44566407799720764},{"id":"https://openalex.org/C2985322473","wikidata":"https://www.wikidata.org/wiki/Q3044843","display_name":"Cancer detection","level":3,"score":0.42058494687080383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41009145975112915},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3917819857597351},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3750658333301544},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.32335737347602844},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.16349148750305176},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13142916560173035},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.08951348066329956}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2993536","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2993536","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09089849.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:adaeba84b20342088e6b30fface67d53","is_oa":true,"landing_page_url":"https://doaj.org/article/adaeba84b20342088e6b30fface67d53","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 96946-96954 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2993536","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2993536","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09089849.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G8510529444","display_name":null,"funder_award_id":"SZSM201811073","funder_id":"https://openalex.org/F4320335767","funder_display_name":"Sanming Project of Medicine in Shenzhen"}],"funders":[{"id":"https://openalex.org/F4320335767","display_name":"Sanming Project of Medicine in Shenzhen","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3023402959.pdf","grobid_xml":"https://content.openalex.org/works/W3023402959.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1570613334","https://openalex.org/W2062177968","https://openalex.org/W2143345254","https://openalex.org/W2190746225","https://openalex.org/W2240965754","https://openalex.org/W2248620004","https://openalex.org/W2339885376","https://openalex.org/W2409650203","https://openalex.org/W2474421929","https://openalex.org/W2555803041","https://openalex.org/W2558340819","https://openalex.org/W2560617289","https://openalex.org/W2606915180","https://openalex.org/W2607075141","https://openalex.org/W2744692634","https://openalex.org/W2745869571","https://openalex.org/W2771248105","https://openalex.org/W2772723798","https://openalex.org/W2787746658","https://openalex.org/W2790813572","https://openalex.org/W2792983091","https://openalex.org/W2809504579","https://openalex.org/W2888804473","https://openalex.org/W2892235178","https://openalex.org/W2900906325","https://openalex.org/W2901743512","https://openalex.org/W2903829201","https://openalex.org/W2928842276","https://openalex.org/W3098150009","https://openalex.org/W6721136770"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W3039673966","https://openalex.org/W4293699968","https://openalex.org/W2002351707","https://openalex.org/W2035096001","https://openalex.org/W2923621274","https://openalex.org/W4380075502","https://openalex.org/W4283313480","https://openalex.org/W4205438228","https://openalex.org/W4390693776"],"abstract_inverted_index":{"Breast":[0,18],"cancer":[1,16,19,50,74,89],"is":[2,52],"one":[3],"of":[4,14,118,203,233],"the":[5,10,29,44,116,129,201,220,229],"most":[6,221],"dangerous":[7],"diseases":[8],"and":[9,33,41,59,160,175,187,206,215,237],"second":[11],"largest":[12],"cause":[13],"female":[15],"death.":[17],"starts":[20,126],"when":[21],"malignant,":[22],"cancerous":[23],"lumps":[24],"start":[25],"to":[26,38,101,133,218,243],"grow":[27],"from":[28],"breast":[30,49,88,135,158],"cells.":[31],"Self-tests":[32],"Periodic":[34],"clinical":[35],"checks":[36],"help":[37],"early":[39],"diagnosis":[40],"thereby":[42],"improve":[43],"survival":[45],"chances":[46],"significantly.":[47],"The":[48,162,173,224],"classification":[51,107,174,214],"a":[53,62,70,183,188],"medical":[54],"method":[55],"that":[56,178,228],"provides":[57],"researchers":[58],"scientists":[60],"with":[61,95,127,200],"great":[63],"challenge.":[64],"Neural":[65],"networks":[66,122],"have":[67],"recently":[68],"become":[69],"popular":[71],"tool":[72],"in":[73,145,182],"data":[75],"classification.":[76],"In":[77,99],"this":[78],"paper,":[79],"Deep":[80],"Learning":[81],"assisted":[82],"Efficient":[83],"Adaboost":[84],"Algorithm":[85],"(DLA-EABA)":[86],"for":[87,137],"detection":[90],"has":[91,179,240],"been":[92,180,241],"mathematically":[93],"proposed":[94],"advanced":[96],"computational":[97],"techniques.":[98],"addition":[100],"traditional":[102],"computer":[103],"vision":[104],"approaches,":[105],"tumor":[106],"methods":[108,202],"using":[109,213],"transfers":[110],"are":[111],"being":[112],"actively":[113],"developed":[114],"through":[115,209],"use":[117],"deep":[119,163],"convolutional":[120,168],"neural":[121],"(CNNs).":[123],"This":[124,191],"study":[125],"examining":[128],"CNN-based":[130],"transfer":[131],"learning":[132,164,198],"characterize":[134],"masses":[136],"different":[138],"diagnostic,":[139],"predictive":[140],"tasks":[141],"or":[142,144],"prognostic":[143],"several":[146,167],"imaging":[147],"modalities,":[148],"such":[149],"as":[150],"Magnetic":[151],"Resonance":[152],"Imaging":[153],"(MRI),":[154],"Ultrasound":[155],"(US),":[156],"digital":[157],"tomosynthesis":[159],"mammography.":[161],"framework":[165],"contains":[166],"layers,":[169],"LSTM,":[170],"Max-pooling":[171],"layers.":[172],"error":[176],"estimation":[177],"included":[181],"fully":[184],"connected":[185],"layer":[186],"softmax":[189],"layer.":[190],"paper":[192],"focuses":[193],"on":[194],"combining":[195],"these":[196],"machine":[197],"approaches":[199],"selecting":[204],"features":[205],"extracting":[207],"them":[208],"evaluating":[210],"their":[211],"output":[212],"segmentation":[216],"techniques":[217],"find":[219],"appropriate":[222],"approach.":[223],"experimental":[225],"results":[226],"show":[227],"high":[230],"accuracy":[231],"level":[232],"97.2%,":[234],"Sensitivity":[235],"98.3%,":[236],"Specificity":[238],"96.5%":[239],"compared":[242],"other":[244],"existing":[245],"systems.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":24},{"year":2025,"cited_by_count":50},{"year":2024,"cited_by_count":85},{"year":2023,"cited_by_count":77},{"year":2022,"cited_by_count":51},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":4}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
