{"id":"https://openalex.org/W4408200868","doi":"https://doi.org/10.1109/cisp-bmei64163.2024.10906260","title":"A Study on an Improved ResNet Model for Image Classification","display_name":"A Study on an Improved ResNet Model for Image Classification","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4408200868","doi":"https://doi.org/10.1109/cisp-bmei64163.2024.10906260"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei64163.2024.10906260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei64163.2024.10906260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104106206","display_name":"Yi Liu","orcid":"https://orcid.org/0009-0000-7570-262X"},"institutions":[{"id":"https://openalex.org/I4210154338","display_name":"Hubei Engineering University","ror":"https://ror.org/05amnwk22","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210154338"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yi Liu","raw_affiliation_strings":["School of Computer and Information Science, HuBei Engineering University,XiaoGan,China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Science, HuBei Engineering University,XiaoGan,China","institution_ids":["https://openalex.org/I4210154338"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103033910","display_name":"Weihu Wang","orcid":"https://orcid.org/0000-0003-4969-398X"},"institutions":[{"id":"https://openalex.org/I4210154338","display_name":"Hubei Engineering University","ror":"https://ror.org/05amnwk22","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210154338"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihu Wang","raw_affiliation_strings":["School of Computer and Information Science, HuBei Engineering University,XiaoGan,China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Science, HuBei Engineering University,XiaoGan,China","institution_ids":["https://openalex.org/I4210154338"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100351700","display_name":"Wenbin He","orcid":"https://orcid.org/0000-0003-1700-6057"},"institutions":[{"id":"https://openalex.org/I4210154338","display_name":"Hubei Engineering University","ror":"https://ror.org/05amnwk22","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210154338"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbin He","raw_affiliation_strings":["College of Technology, HuBei Engineering University,XiaoGan,China"],"affiliations":[{"raw_affiliation_string":"College of Technology, HuBei Engineering University,XiaoGan,China","institution_ids":["https://openalex.org/I4210154338"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5104106206"],"corresponding_institution_ids":["https://openalex.org/I4210154338"],"apc_list":null,"apc_paid":null,"fwci":1.6237,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.8924049,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14484","display_name":"Technology and Data Analysis","score":0.37700000405311584,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14484","display_name":"Technology and Data Analysis","score":0.37700000405311584,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14510","display_name":"Medical Imaging and Analysis","score":0.3716000020503998,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.7485673427581787},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5998045206069946},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5527893900871277},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.532964289188385},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48382988572120667},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4501384496688843},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3333663046360016},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2390700876712799}],"concepts":[{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.7485673427581787},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5998045206069946},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5527893900871277},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.532964289188385},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48382988572120667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4501384496688843},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3333663046360016},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2390700876712799}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei64163.2024.10906260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei64163.2024.10906260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2777186991","https://openalex.org/W2995942064","https://openalex.org/W2996367417","https://openalex.org/W3083622693","https://openalex.org/W3086928989","https://openalex.org/W3160382941","https://openalex.org/W3174025757","https://openalex.org/W3216623900","https://openalex.org/W4376256497","https://openalex.org/W4382241500","https://openalex.org/W4386497355","https://openalex.org/W4387230345","https://openalex.org/W4391211384","https://openalex.org/W4391953434","https://openalex.org/W4392044250","https://openalex.org/W4392450177","https://openalex.org/W4394625657","https://openalex.org/W4397022468","https://openalex.org/W4401450512","https://openalex.org/W6850265582"],"related_works":["https://openalex.org/W2599472179","https://openalex.org/W4323057981","https://openalex.org/W3178607569","https://openalex.org/W4308408209","https://openalex.org/W4401359364","https://openalex.org/W2995001409","https://openalex.org/W2546503577","https://openalex.org/W4396918490","https://openalex.org/W4393269152","https://openalex.org/W4212887358"],"abstract_inverted_index":{"ResNet":[0],"effectively":[1],"addresses":[2],"the":[3,78,87,91,105,116,126,133,137,144,149,161],"vanishing":[4],"gradient":[5],"problem":[6],"in":[7,19,154],"deep":[8],"networks":[9],"by":[10,74],"introducing":[11],"skip":[12],"connections,":[13],"making":[14],"it":[15],"a":[16,164],"leading":[17],"model":[18,29,108],"image":[20,179],"classification":[21,180],"tasks.":[22],"This":[23,158],"paper":[24],"proposes":[25],"an":[26,55],"improved":[27,57,99,107],"ResNet-based":[28],"leveraging":[30],"transfer":[31,94,169],"learning":[32],"from":[33],"ImageNet,":[34],"initially":[35],"incorporating":[36],"additional":[37],"layers":[38],"such":[39,121],"as":[40,122],"Flatten,":[41],"fully":[42],"connected":[43],"layers,":[44],"L2":[45],"regularization,":[46],"Batch":[47],"Normalization,":[48],"and":[49,65,97,112,147,173],"Dropout.":[50],"The":[51,101],"research":[52],"began":[53],"with":[54],"\u201cinitial":[56],"version\u201d":[58],"of":[59,81,163],"ResNet50,":[60,93],"designed":[61],"to":[62,115,177],"enhance":[63,178],"stability":[64,111],"generalization.":[66],"Subsequent":[67],"experiments":[68,85],"focused":[69],"on":[70,86,136],"further":[71],"optimizations":[72],"inspired":[73],"recent":[75],"studies,":[76],"particularly":[77],"strategic":[79,174],"placement":[80,176],"Dropout":[82,124,142,175],"layers.":[83],"Comparative":[84],"CIFAR-10":[88],"dataset":[89],"included":[90],"original":[92,117],"learning-enhanced":[95],"ResNet,":[96],"multiple":[98],"variants.":[100],"results":[102],"demonstrate":[103],"that":[104],"initial":[106],"significantly":[109],"enhances":[110],"generalization":[113],"compared":[114],"ResNet.":[118],"Further":[119],"optimizations,":[120],"placing":[123,141],"before":[125,148],"Global":[127],"Average":[128],"Pooling":[129],"(GAP)":[130],"layer,":[131],"yielded":[132],"best":[134],"performance":[135],"training":[138],"set,":[139],"while":[140],"after":[143],"Flatten":[145],"layer":[146,152],"Dense":[150],"(128)":[151],"resulted":[153],"superior":[155],"validation":[156],"performance.":[157,181],"study":[159],"underscores":[160],"benefits":[162],"stepwise":[165],"optimization":[166],"approach,":[167],"combining":[168],"learning,":[170],"structural":[171],"modifications,":[172]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}