{"id":"https://openalex.org/W4400975209","doi":"https://doi.org/10.1109/access.2024.3433483","title":"An Evolutionary Deep Learning Method Based on Improved Heap-Based Optimization for Medical Image Classification and Diagnosis","display_name":"An Evolutionary Deep Learning Method Based on Improved Heap-Based Optimization for Medical Image Classification and Diagnosis","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400975209","doi":"https://doi.org/10.1109/access.2024.3433483"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3433483","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3433483","pdf_url":null,"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":null,"license_id":null,"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://doi.org/10.1109/access.2024.3433483","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100889409","display_name":"Lin Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I2802939634","display_name":"Chinese PLA General Hospital","ror":"https://ror.org/04gw3ra78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2802939634"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["Department of Respiratory and Critical Care Medicine, First Medical Centre of Chinese PLA General Hospital, Haidian, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Respiratory and Critical Care Medicine, First Medical Centre of Chinese PLA General Hospital, Haidian, Beijing, China","institution_ids":["https://openalex.org/I2802939634"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077170611","display_name":"Zenglin Qiao","orcid":"https://orcid.org/0000-0002-3793-7150"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zenglin Qiao","raw_affiliation_strings":["College of Science, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Science, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106580416","display_name":"Lina Li","orcid":null},"institutions":[{"id":"https://openalex.org/I2802939634","display_name":"Chinese PLA General Hospital","ror":"https://ror.org/04gw3ra78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2802939634"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lina Li","raw_affiliation_strings":["Department of Respiratory and Critical Care Medicine, First Medical Centre of Chinese PLA General Hospital, Haidian, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Respiratory and Critical Care Medicine, First Medical Centre of Chinese PLA General Hospital, Haidian, Beijing, China","institution_ids":["https://openalex.org/I2802939634"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100889409"],"corresponding_institution_ids":["https://openalex.org/I2802939634"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":5.0919,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.9592932,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"12","issue":null,"first_page":"102745","last_page":"102773"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9958000183105469,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9958000183105469,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9957000017166138,"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":0.989300012588501,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7869015336036682},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7774803638458252},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7723429203033447},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7667039632797241},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.608762800693512},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5311958193778992},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.49504604935646057},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4875204265117645},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.461626797914505},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4465986490249634},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.43488967418670654},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42986077070236206},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.42277759313583374},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2741560637950897},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21065527200698853}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7869015336036682},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7774803638458252},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7723429203033447},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7667039632797241},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.608762800693512},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5311958193778992},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.49504604935646057},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4875204265117645},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.461626797914505},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4465986490249634},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.43488967418670654},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42986077070236206},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.42277759313583374},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2741560637950897},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21065527200698853},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3433483","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3433483","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e3f58f3516d34624a8813bc684af7e34","is_oa":true,"landing_page_url":"https://doaj.org/article/e3f58f3516d34624a8813bc684af7e34","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 12, Pp 102745-102773 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3433483","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3433483","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2061438946","https://openalex.org/W2078324766","https://openalex.org/W2097117768","https://openalex.org/W2165466912","https://openalex.org/W2194775991","https://openalex.org/W2435090885","https://openalex.org/W2618530766","https://openalex.org/W2801670578","https://openalex.org/W2898785807","https://openalex.org/W2907479996","https://openalex.org/W2929255206","https://openalex.org/W2982459439","https://openalex.org/W2989363164","https://openalex.org/W2997546679","https://openalex.org/W3043211958","https://openalex.org/W3114893929","https://openalex.org/W3198810028","https://openalex.org/W4200409353","https://openalex.org/W4210642693","https://openalex.org/W4210655294","https://openalex.org/W4220685350","https://openalex.org/W4285495304","https://openalex.org/W4297326478","https://openalex.org/W4309198966","https://openalex.org/W4309891817","https://openalex.org/W4316661284","https://openalex.org/W4318815113","https://openalex.org/W4321502729","https://openalex.org/W4322724513","https://openalex.org/W4366824900","https://openalex.org/W4366988556","https://openalex.org/W4379508959","https://openalex.org/W4384153676","https://openalex.org/W4386850943","https://openalex.org/W4387675520","https://openalex.org/W4390509379","https://openalex.org/W4391503724","https://openalex.org/W4392246968","https://openalex.org/W4394966997","https://openalex.org/W6964665992"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W3183136280","https://openalex.org/W4318559728","https://openalex.org/W2775233965","https://openalex.org/W4360995913","https://openalex.org/W4312193868"],"abstract_inverted_index":{"With":[0],"the":[1,12,30,41,48,59,111,125,128,143,146,155,161,182,201,232],"continuous":[2],"advancement":[3],"of":[4,16,32,51,61,127,145,167,181],"deep":[5,9,33,62,69,80,129,185],"neural":[6,63,82],"networks,":[7],"leveraging":[8],"learning":[10,34,70,103,186],"for":[11,72,160],"classification":[13,229],"and":[14,101,109,116,135,173,208,222,238],"diagnosis":[15,24],"medical":[17,73,138,192,197],"images":[18],"to":[19,105,123],"aid":[20],"physicians":[21],"in":[22,137],"patient":[23],"has":[25],"gained":[26],"significant":[27],"traction.":[28],"However,":[29],"efficacy":[31,144],"networks":[35],"is":[36,121,188],"not":[37],"solely":[38],"contingent":[39],"upon":[40],"network":[42,83],"architecture,":[43],"it":[44],"also":[45],"relies":[46],"on":[47,154,231],"appropriate":[49],"setting":[50],"hyperparameters.":[52],"Properly":[53],"tuning":[54],"hyperparameters":[55,126],"can":[56],"markedly":[57],"enhance":[58],"performance":[60,180],"networks.":[64],"To":[65,141],"propose":[66],"an":[67,86],"efficient":[68],"method":[71,187],"image":[74,139,193,198,211],"classification,":[75],"this":[76,149],"study":[77,150],"introduces":[78],"a":[79],"residual":[81,130],"optimized":[84],"using":[85],"enhanced":[87,93],"Heap-based":[88],"Optimization":[89],"(HBO)":[90],"algorithm.":[91],"The":[92,118,179,195,228],"HBO":[94,163],"algorithm":[95,107,120],"integrates":[96],"elements":[97],"grey":[98],"wolf":[99],"mechanism":[100,104],"orthogonal":[102],"augment":[106],"convergence":[108],"bolster":[110],"equilibrium":[112],"between":[113],"global":[114],"exploration":[115],"exploitation.":[117],"proposed":[119,147,183],"applied":[122],"optimize":[124],"network,":[131],"thereby":[132],"enhancing":[133],"accuracy":[134],"reliability":[136],"classification.":[140],"evaluate":[142],"model,":[148],"conducts":[151],"various":[152],"experiments":[153],"CEC":[156],"2017":[157],"benchmark":[158],"functions":[159],"updated":[162],"algorithm,":[164],"encompassing":[165],"validation":[166],"strategies,":[168],"scalability":[169],"assessments,":[170],"balance":[171],"analyses,":[172],"comparisons":[174],"with":[175],"state-of-the-art":[176],"similar":[177],"algorithms.":[178,244],"evolutionary":[184],"assessed":[189],"across":[190],"three":[191,196,233],"datasets.":[194],"datasets":[199,214,234],"encompass":[200],"colorectal":[202],"cancer":[203],"dataset,":[204,207],"brain":[205],"tumor":[206],"chest":[209],"X-ray":[210],"dataset.":[212],"These":[213],"represent":[215],"tissue":[216],"images,":[217,226],"magnetic":[218],"resonance":[219],"imaging":[220],"(MRI),":[221],"computed":[223],"tomography":[224],"(CT)":[225],"respectively.":[227],"accuracies":[230],"reached":[235],"95.6%,":[236],"97.64%,":[237],"96.48%,":[239],"respectively,":[240],"outperforming":[241],"other":[242],"competing":[243]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
