{"id":"https://openalex.org/W4285273012","doi":"https://doi.org/10.1109/access.2022.3182399","title":"An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks","display_name":"An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285273012","doi":"https://doi.org/10.1109/access.2022.3182399"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3182399","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3182399","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09794729.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/ielx7/6287639/6514899/09794729.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036358389","display_name":"Dominik M\u00fcller","orcid":"https://orcid.org/0000-0003-0838-9885"},"institutions":[{"id":"https://openalex.org/I179225836","display_name":"University of Augsburg","ror":"https://ror.org/03p14d497","country_code":"DE","type":"education","lineage":["https://openalex.org/I179225836"]},{"id":"https://openalex.org/I2802007660","display_name":"University Hospital Augsburg","ror":"https://ror.org/03b0k9c14","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I2802007660"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Dominik Muller","raw_affiliation_strings":["IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany","Institute for Digital Medicine, University Hospital Augsburg, Germany"],"raw_orcid":"https://orcid.org/0000-0003-0838-9885","affiliations":[{"raw_affiliation_string":"IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I179225836"]},{"raw_affiliation_string":"Institute for Digital Medicine, University Hospital Augsburg, Germany","institution_ids":["https://openalex.org/I2802007660"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045634598","display_name":"I\u00f1aki Soto\u2010Rey","orcid":"https://orcid.org/0000-0003-3061-5818"},"institutions":[{"id":"https://openalex.org/I2802007660","display_name":"University Hospital Augsburg","ror":"https://ror.org/03b0k9c14","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I2802007660"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Inaki Soto-Rey","raw_affiliation_strings":["Medical Data Integration Center, Institute for Digital Medicine, University Hospital Augsburg, Augsburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Medical Data Integration Center, Institute for Digital Medicine, University Hospital Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I2802007660"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010964383","display_name":"Frank Kr\u00e4mer","orcid":"https://orcid.org/0000-0002-2857-7122"},"institutions":[{"id":"https://openalex.org/I179225836","display_name":"University of Augsburg","ror":"https://ror.org/03p14d497","country_code":"DE","type":"education","lineage":["https://openalex.org/I179225836"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Frank Kramer","raw_affiliation_strings":["IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany"],"raw_orcid":"https://orcid.org/0000-0002-2857-7122","affiliations":[{"raw_affiliation_string":"IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I179225836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036358389"],"corresponding_institution_ids":["https://openalex.org/I179225836","https://openalex.org/I2802007660"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":15.4485,"has_fulltext":true,"cited_by_count":114,"citation_normalized_percentile":{"value":0.99574635,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"10","issue":null,"first_page":"66467","last_page":"66480"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9988999962806702,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7770280241966248},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7530538439750671},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6653107404708862},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.656326174736023},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6513041853904724},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6390165686607361},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6258716583251953},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6084897518157959},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6059689521789551},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5438521504402161},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5090965628623962},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.49757125973701477},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.4759078919887543},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.47027426958084106},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43920421600341797},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4220760762691498},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32371634244918823},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30104148387908936}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7770280241966248},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7530538439750671},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6653107404708862},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.656326174736023},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6513041853904724},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6390165686607361},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6258716583251953},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6084897518157959},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6059689521789551},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5438521504402161},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5090965628623962},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.49757125973701477},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.4759078919887543},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.47027426958084106},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43920421600341797},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4220760762691498},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32371634244918823},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30104148387908936},{"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/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2022.3182399","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3182399","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09794729.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:d68cc133c4a64bb48af187014a5e5b1c","is_oa":true,"landing_page_url":"https://doaj.org/article/d68cc133c4a64bb48af187014a5e5b1c","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 66467-66480 (2022)","raw_type":"article"},{"id":"pmh:oai:uni-augsburg.opus-bayern.de:96791","is_oa":true,"landing_page_url":"https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/96791","pdf_url":null,"source":{"id":"https://openalex.org/S4306400930","display_name":"OPUS (Augsburg University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I119916105","host_organization_name":"Augsburg University","host_organization_lineage":["https://openalex.org/I119916105"],"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":null,"raw_type":"doc-type:article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3182399","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3182399","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09794729.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":[],"awards":[{"id":"https://openalex.org/G8285651077","display_name":null,"funder_award_id":"FKZ01ZZ1804E","funder_id":"https://openalex.org/F4320321114","funder_display_name":"Bundesministerium f\u00fcr Bildung und Forschung"}],"funders":[{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4285273012.pdf","grobid_xml":"https://content.openalex.org/works/W4285273012.grobid-xml"},"referenced_works_count":86,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W2005126631","https://openalex.org/W2076118331","https://openalex.org/W2101234009","https://openalex.org/W2115531174","https://openalex.org/W2115629999","https://openalex.org/W2117539524","https://openalex.org/W2153635508","https://openalex.org/W2158698691","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2342249984","https://openalex.org/W2435090885","https://openalex.org/W2484065175","https://openalex.org/W2520016695","https://openalex.org/W2531409750","https://openalex.org/W2533800772","https://openalex.org/W2549139847","https://openalex.org/W2592929672","https://openalex.org/W2607306668","https://openalex.org/W2621042378","https://openalex.org/W2789758093","https://openalex.org/W2794825826","https://openalex.org/W2884561390","https://openalex.org/W2889985731","https://openalex.org/W2895926103","https://openalex.org/W2898280479","https://openalex.org/W2910489404","https://openalex.org/W2911964244","https://openalex.org/W2922975151","https://openalex.org/W2940090474","https://openalex.org/W2948954099","https://openalex.org/W2954996726","https://openalex.org/W2955425717","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2963946669","https://openalex.org/W2964416181","https://openalex.org/W3004868960","https://openalex.org/W3007943565","https://openalex.org/W3008097860","https://openalex.org/W3013277995","https://openalex.org/W3034124771","https://openalex.org/W3036319923","https://openalex.org/W3036688711","https://openalex.org/W3038255799","https://openalex.org/W3047083963","https://openalex.org/W3048545344","https://openalex.org/W3049002444","https://openalex.org/W3097787522","https://openalex.org/W3099319035","https://openalex.org/W3102785203","https://openalex.org/W3135005580","https://openalex.org/W3135057764","https://openalex.org/W3140743785","https://openalex.org/W3140854437","https://openalex.org/W3149839747","https://openalex.org/W3164405530","https://openalex.org/W3174735939","https://openalex.org/W3190897131","https://openalex.org/W3192519463","https://openalex.org/W3195743132","https://openalex.org/W3200963996","https://openalex.org/W3202201423","https://openalex.org/W3203337527","https://openalex.org/W3215618442","https://openalex.org/W4200362906","https://openalex.org/W4214592090","https://openalex.org/W4377862871","https://openalex.org/W4378767342","https://openalex.org/W6631190155","https://openalex.org/W6636906085","https://openalex.org/W6675354045","https://openalex.org/W6681046083","https://openalex.org/W6683036876","https://openalex.org/W6738486038","https://openalex.org/W6755053738","https://openalex.org/W6762718338","https://openalex.org/W6763204649","https://openalex.org/W6763723203","https://openalex.org/W6766394743","https://openalex.org/W6774375107","https://openalex.org/W6774725951","https://openalex.org/W6793364036","https://openalex.org/W6801461832"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W2748667022","https://openalex.org/W3211770882","https://openalex.org/W2010629964","https://openalex.org/W2983135586"],"abstract_inverted_index":{"Novel":[0],"and":[1,82,90,168,237],"high-performance":[2],"medical":[3,54,65,109,230],"image":[4,55,66,91,231],"classification":[5,56,67,232],"pipelines":[6],"are":[7,48,204],"heavily":[8],"utilizing":[9],"ensemble":[10,16,45,77,221],"learning":[11,17,46,52,78,222],"strategies.":[12],"The":[13,84],"idea":[14],"of":[15,74,87,153,220],"is":[18,33,169,224],"to":[19,38,134,155,166,172,185,194,234],"assemble":[20],"diverse":[21],"models":[22],"or":[23,206],"multiple":[24,121],"predictions":[25,122],"and,":[26],"thus,":[27],"boost":[28,238],"prediction":[29],"performance.":[30,239],"However,":[31],"it":[32],"still":[34],"an":[35,190],"open":[36],"question":[37],"what":[39],"extent":[40],"as":[41,43,94,96],"well":[42,95],"which":[44,187],"strategies":[47],"beneficial":[49],"in":[50,189],"deep":[51,98],"based":[53,175,178],"pipelines.":[57,176],"In":[58],"this":[59],"work,":[60],"we":[61,197],"proposed":[62],"a":[63,225],"reproducible":[64],"pipeline":[68,85,233],"for":[69,119,228],"analyzing":[70],"the":[71,75,149,218],"performance":[72,151,182],"impact":[73],"following":[76],"techniques:":[79],"Augmenting,":[80],"Stacking,":[81,186],"Bagging.":[83],"consists":[86],"state-of-the-art":[88],"preprocessing":[89],"augmentation":[92],"methods":[93],"9":[97],"convolution":[99],"neural":[100],"network":[101],"architectures.":[102],"It":[103],"was":[104],"applied":[105],"on":[106],"four":[107],"popular":[108],"imaging":[110],"datasets":[111],"with":[112],"varying":[113],"complexity.":[114],"Furthermore,":[115,196],"12":[116],"pooling":[117,202,213],"functions":[118,129,138,203],"combining":[120],"were":[123],"analyzed,":[124],"ranging":[125],"from":[126],"simple":[127,200],"statistical":[128,201],"like":[130,139],"unweighted":[131],"averaging":[132],"up":[133,154,165,193],"more":[135,211],"complex":[136,212],"learning-based":[137],"support":[140],"vector":[141],"machines.":[142],"Our":[143],"results":[144],"revealed":[145],"that":[146,199,217],"Stacking":[147],"achieved":[148],"largest":[150],"gain":[152,183],"13%":[156],"F1-score":[157,191],"increase.":[158],"Augmenting":[159],"showed":[160],"consistent":[161],"improvement":[162],"capabilities":[163],"by":[164],"4%":[167],"also":[170],"applicable":[171],"single":[173],"model":[174],"Cross-validation":[177],"Bagging":[179],"demonstrated":[180,198],"significant":[181],"close":[184],"resulted":[188],"increase":[192],"+11%.":[195],"equal":[205],"often":[207],"even":[208],"better":[209],"than":[210],"functions.":[214],"We":[215],"concluded":[216],"integration":[219],"techniques":[223],"powerful":[226],"method":[227],"any":[229],"improve":[235],"robustness":[236]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":56},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}