{"id":"https://openalex.org/W4387459728","doi":"https://doi.org/10.3389/frai.2023.1232640","title":"Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting","display_name":"Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting","publication_year":2023,"publication_date":"2023-10-09","ids":{"openalex":"https://openalex.org/W4387459728","doi":"https://doi.org/10.3389/frai.2023.1232640","pmid":"https://pubmed.ncbi.nlm.nih.gov/37876961"},"language":"en","primary_location":{"id":"doi:10.3389/frai.2023.1232640","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2023.1232640","pdf_url":"https://www.frontiersin.org/articles/10.3389/frai.2023.1232640/pdf?isPublishedV2=False","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/articles/10.3389/frai.2023.1232640/pdf?isPublishedV2=False","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066382111","display_name":"Wilson Bakasa","orcid":"https://orcid.org/0000-0003-0927-1301"},"institutions":[{"id":"https://openalex.org/I95023434","display_name":"University of KwaZulu-Natal","ror":"https://ror.org/04qzfn040","country_code":"ZA","type":"education","lineage":["https://openalex.org/I95023434"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Wilson Bakasa","raw_affiliation_strings":["School of Mathematics Statistics & Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics Statistics & Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban, South Africa","institution_ids":["https://openalex.org/I95023434"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054315649","display_name":"Serestina Viriri","orcid":"https://orcid.org/0000-0002-2850-8645"},"institutions":[{"id":"https://openalex.org/I95023434","display_name":"University of KwaZulu-Natal","ror":"https://ror.org/04qzfn040","country_code":"ZA","type":"education","lineage":["https://openalex.org/I95023434"]}],"countries":["ZA"],"is_corresponding":true,"raw_author_name":"Serestina Viriri","raw_affiliation_strings":["School of Mathematics Statistics & Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics Statistics & Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban, South Africa","institution_ids":["https://openalex.org/I95023434"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5054315649"],"corresponding_institution_ids":["https://openalex.org/I95023434"],"apc_list":{"value":2125,"currency":"USD","value_usd":2125},"apc_paid":{"value":2125,"currency":"USD","value_usd":2125},"fwci":3.7177,"has_fulltext":true,"cited_by_count":24,"citation_normalized_percentile":{"value":0.94200617,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"6","issue":null,"first_page":"1232640","last_page":"1232640"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9957000017166138,"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/T10231","display_name":"Pancreatic and Hepatic Oncology Research","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.7763619422912598},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7535504102706909},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6535292863845825},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.597644567489624},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5666362047195435},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5540913343429565},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5535938739776611},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5462604761123657},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5406740307807922},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.540336012840271},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5004453659057617},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.40604960918426514},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39935198426246643}],"concepts":[{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7763619422912598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7535504102706909},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6535292863845825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.597644567489624},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5666362047195435},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5540913343429565},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5535938739776611},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5462604761123657},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5406740307807922},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.540336012840271},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5004453659057617},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.40604960918426514},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39935198426246643},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/frai.2023.1232640","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2023.1232640","pdf_url":"https://www.frontiersin.org/articles/10.3389/frai.2023.1232640/pdf?isPublishedV2=False","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},{"id":"pmid:37876961","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37876961","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in artificial intelligence","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10591225","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10591225","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10591225/pdf/frai-06-1232640.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Artif Intell","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:4a73b63fb5904b3ea396fd6b266a3f3c","is_oa":true,"landing_page_url":"https://doaj.org/article/4a73b63fb5904b3ea396fd6b266a3f3c","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":"Frontiers in Artificial Intelligence, Vol 6 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/frai.2023.1232640","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2023.1232640","pdf_url":"https://www.frontiersin.org/articles/10.3389/frai.2023.1232640/pdf?isPublishedV2=False","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387459728.pdf"},"referenced_works_count":91,"referenced_works":["https://openalex.org/W2520016695","https://openalex.org/W2592929672","https://openalex.org/W2617669016","https://openalex.org/W2789758093","https://openalex.org/W2899037650","https://openalex.org/W2900954917","https://openalex.org/W2909098907","https://openalex.org/W2910261017","https://openalex.org/W2911982216","https://openalex.org/W2918408501","https://openalex.org/W2920302751","https://openalex.org/W2921766025","https://openalex.org/W2943903955","https://openalex.org/W2947411064","https://openalex.org/W2953914369","https://openalex.org/W2955805844","https://openalex.org/W2963480753","https://openalex.org/W2966126335","https://openalex.org/W2972168296","https://openalex.org/W2972705814","https://openalex.org/W2979487364","https://openalex.org/W2995589713","https://openalex.org/W2998641003","https://openalex.org/W2998957378","https://openalex.org/W3004666248","https://openalex.org/W3012393889","https://openalex.org/W3012867892","https://openalex.org/W3013699712","https://openalex.org/W3014921497","https://openalex.org/W3028778360","https://openalex.org/W3034124771","https://openalex.org/W3034143844","https://openalex.org/W3035269684","https://openalex.org/W3036688711","https://openalex.org/W3040660552","https://openalex.org/W3042356186","https://openalex.org/W3042648368","https://openalex.org/W3046991059","https://openalex.org/W3049002444","https://openalex.org/W3049505831","https://openalex.org/W3074741277","https://openalex.org/W3083622693","https://openalex.org/W3091707190","https://openalex.org/W3095986980","https://openalex.org/W3096338035","https://openalex.org/W3103635657","https://openalex.org/W3104865655","https://openalex.org/W3105282616","https://openalex.org/W3119632172","https://openalex.org/W3124016887","https://openalex.org/W3124375766","https://openalex.org/W3128040422","https://openalex.org/W3134267336","https://openalex.org/W3134475970","https://openalex.org/W3135005580","https://openalex.org/W3135779218","https://openalex.org/W3136370240","https://openalex.org/W3136950372","https://openalex.org/W3140743785","https://openalex.org/W3144777302","https://openalex.org/W3149839747","https://openalex.org/W3169203486","https://openalex.org/W3174735939","https://openalex.org/W3183312143","https://openalex.org/W3183778643","https://openalex.org/W3188404242","https://openalex.org/W3197288968","https://openalex.org/W3204527981","https://openalex.org/W3208787555","https://openalex.org/W3215490101","https://openalex.org/W4200126462","https://openalex.org/W4210670268","https://openalex.org/W4213429304","https://openalex.org/W4220998430","https://openalex.org/W4232935862","https://openalex.org/W4285273012","https://openalex.org/W4285333098","https://openalex.org/W4285799383","https://openalex.org/W4293004928","https://openalex.org/W4293203439","https://openalex.org/W4309375094","https://openalex.org/W4309744061","https://openalex.org/W4309947337","https://openalex.org/W4324142429","https://openalex.org/W4393982871","https://openalex.org/W6760324639","https://openalex.org/W6767780810","https://openalex.org/W6768230505","https://openalex.org/W6774872334","https://openalex.org/W6791283100","https://openalex.org/W6793364036"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W4310224730","https://openalex.org/W4296079469"],"abstract_inverted_index":{"Ensemble":[0,37,75],"learning":[1,19,47],"aims":[2],"to":[3,44,59,153,211],"improve":[4,60],"prediction":[5],"performance":[6,63,218],"by":[7],"combining":[8,56,103],"several":[9],"models":[10,54],"or":[11,231],"forecasts.":[12],"However,":[13],"how":[14],"much":[15],"and":[16,55,94,96,181,190,197,215],"which":[17],"ensemble":[18,114,148],"techniques":[20,50],"are":[21,39,90],"useful":[22],"in":[23],"deep":[24],"learning-based":[25],"pipelines":[26],"for":[27,81,111,195,222],"pancreas":[28,83,167,224],"computed":[29],"tomography":[30],"(CT)":[31],"image":[32],"classification":[33],"is":[34,122,207],"a":[35,65,79,99,130,146,171,237],"challenge.":[36],"approaches":[38],"the":[40,61,71,104,113,117,135,154,204,213,217,220],"most":[41],"advanced":[42],"solution":[43],"many":[45],"machine":[46],"problems.":[48],"These":[49],"entail":[51],"training":[52,196],"multiple":[53],"their":[57],"predictions":[58],"predictive":[62],"of":[64,73,120,165,173,187,219],"single":[66],"model.":[67],"This":[68],"article":[69],"introduces":[70],"idea":[72],"Stacked":[74],"Deep":[76],"Learning":[77],"(SEDL),":[78],"pipeline":[80,221],"classifying":[82,223],"CT":[84,168,225],"medical":[85,226],"images.":[86,227],"The":[87,156],"weak":[88],"learners":[89,233],"Inception":[91],"V3,":[92],"VGG16,":[93],"ResNet34,":[95],"we":[97],"employed":[98,128],"stacking":[100],"ensemble.":[101],"By":[102],"first-level":[105],"predictions,":[106],"an":[107,208],"input":[108],"train":[109],"set":[110],"XGBoost,":[112],"model":[115],"at":[116],"second":[118],"level":[119],"prediction,":[121],"created.":[123],"Extreme":[124],"Gradient":[125],"Boosting":[126],"(XGBoost),":[127],"as":[129],"strong":[131],"learner,":[132],"will":[133],"make":[134,236],"final":[136],"classification.":[137],"Our":[138],"findings":[139],"showed":[140],"that":[141,202],"SEDL":[142,205],"performed":[143],"better,":[144],"with":[145,170],"98.8%":[147],"accuracy,":[149],"after":[150],"some":[151],"adjustments":[152],"hyperparameters.":[155],"Cancer":[157],"Imaging":[158],"Archive":[159],"(TCIA)":[160],"public":[161],"access":[162],"dataset":[163],"consists":[164],"80":[166],"scans":[169],"resolution":[172],"512":[174,176],"*":[175],"pixels,":[177],"from":[178],"53":[179],"male":[180],"27":[182],"female":[183],"subjects.":[184],"A":[185],"sample":[186],"two":[188],"hundred":[189],"twenty-two":[191],"images":[192],"was":[193],"used":[194],"testing":[198],"data.":[199],"We":[200],"concluded":[201],"implementing":[203],"technique":[206],"effective":[209],"way":[210],"strengthen":[212],"robustness":[214],"increase":[216],"Interestingly,":[228],"grouping":[229],"like-minded":[230],"talented":[232],"does":[234],"not":[235],"difference.":[238]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":6}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
