{"id":"https://openalex.org/W7124161459","doi":"https://doi.org/10.1145/3777577.3777724","title":"Detection of Pediatric Pneumonia Based on DenseFPN and Hybrid Channel Attention","display_name":"Detection of Pediatric Pneumonia Based on DenseFPN and Hybrid Channel Attention","publication_year":2025,"publication_date":"2025-10-24","ids":{"openalex":"https://openalex.org/W7124161459","doi":"https://doi.org/10.1145/3777577.3777724"},"language":null,"primary_location":{"id":"doi:10.1145/3777577.3777724","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777724","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3777577.3777724","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025201374","display_name":"Yang Tian","orcid":"https://orcid.org/0000-0003-2642-4760"},"institutions":[{"id":"https://openalex.org/I4210119246","display_name":"Department of Science and Technology of Liaoning Province","ror":"https://ror.org/02f09cx53","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210119246","https://openalex.org/I4405254576"]},{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuan Tian","raw_affiliation_strings":["Department of Computer Technology, School of Computer Science and Engineering, Shenyang, Liaoning, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Technology, School of Computer Science and Engineering, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I4210119246","https://openalex.org/I157507598"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123006927","display_name":"Zhixin Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]},{"id":"https://openalex.org/I4210119246","display_name":"Department of Science and Technology of Liaoning Province","ror":"https://ror.org/02f09cx53","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210119246","https://openalex.org/I4405254576"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixin Cheng","raw_affiliation_strings":["Department of Computer Technology, School of Computer Science and Engineering, Shenyang, Liaoning, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Technology, School of Computer Science and Engineering, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I4210119246","https://openalex.org/I157507598"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025201374"],"corresponding_institution_ids":["https://openalex.org/I157507598","https://openalex.org/I4210119246"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72319077,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"899","last_page":"904"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9717000126838684,"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":0.9717000126838684,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.003000000026077032,"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/T10862","display_name":"AI in cancer detection","score":0.0026000000070780516,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7335000038146973},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6693000197410583},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.6299999952316284},{"id":"https://openalex.org/keywords/pneumonia","display_name":"Pneumonia","score":0.6085000038146973},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4453999996185303},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3952000141143799},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.33309999108314514},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.32820001244544983}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7335000038146973},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6693000197410583},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.6299999952316284},{"id":"https://openalex.org/C2777914695","wikidata":"https://www.wikidata.org/wiki/Q12192","display_name":"Pneumonia","level":2,"score":0.6085000038146973},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5999000072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5532000064849854},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4453999996185303},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.396699994802475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.387800008058548},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.31779998540878296},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26930001378059387},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25929999351501465}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3777577.3777724","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777724","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3777577.3777724","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777724","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.7071919441223145}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2752782242","https://openalex.org/W2884261459","https://openalex.org/W3012303644","https://openalex.org/W3034552520","https://openalex.org/W3070377305","https://openalex.org/W3140854437","https://openalex.org/W3167147947","https://openalex.org/W4200551431","https://openalex.org/W4238732474","https://openalex.org/W4312264311","https://openalex.org/W4321213886","https://openalex.org/W4410081766","https://openalex.org/W4410395987"],"related_works":[],"abstract_inverted_index":{"Pneumonia,":[0],"as":[1,103],"a":[2,95,178],"highly":[3,43],"prevalent":[4],"and":[5,11,42,59,79,107,119,135,154,173],"fatal":[6],"respiratory":[7],"disease,":[8],"requires":[9],"early":[10],"accurate":[12],"diagnosis":[13],"to":[14,35,38,68,75],"significantly":[15],"improve":[16],"clinical":[17,128],"treatment":[18],"outcomes.":[19],"Convolutional":[20],"neural":[21],"networks":[22],"(CNNs)":[23],"have":[24],"demonstrated":[25],"remarkable":[26],"potential":[27],"in":[28,48,54,156,194],"the":[29,40,60,81,89,104,117,138,149,190],"automatic":[30],"detection":[31,158,196],"of":[32,64,83,121,182],"pneumonia":[33,122,142,157,195],"due":[34],"their":[36],"ability":[37],"handle":[39],"multi-scale":[41],"heterogeneous":[44],"lesion":[45,77],"characteristics":[46],"observed":[47],"chest":[49],"X-ray":[50],"images.":[51],"However,":[52],"variations":[53],"imaging":[55],"conditions,":[56],"patient":[57],"positioning,":[58],"unique":[61],"anatomical":[62],"features":[63],"pediatric":[65],"lungs":[66],"lead":[67],"inconsistent":[69],"image":[70,143],"quality,":[71],"making":[72],"it":[73],"challenging":[74],"capture":[76],"details":[78],"limiting":[80],"performance":[82],"traditional":[84],"CNN":[85],"models.":[86],"To":[87],"address":[88],"aforementioned":[90],"challenges,":[91],"this":[92],"study":[93],"proposes":[94],"semi-supervised":[96],"deep":[97],"learning":[98],"framework":[99],"that":[100,148],"employs":[101],"DenseFPN":[102],"backbone":[105],"network":[106],"incorporates":[108],"Hybrid":[109],"Channel":[110],"Attention":[111],"(HCA).":[112],"This":[113],"approach":[114],"effectively":[115],"enhances":[116],"accuracy":[118,153,167],"robustness":[120,155],"detection,":[123],"providing":[124],"robust":[125],"support":[126],"for":[127],"auxiliary":[129],"diagnosis.":[130],"The":[131],"model":[132,150],"was":[133],"trained":[134],"validated":[136],"using":[137],"publicly":[139],"available":[140],"ChestXRay2017":[141],"dataset.":[144],"Experimental":[145],"results":[146],"demonstrate":[147,189],"achieves":[151],"high":[152],"tasks,":[159],"exhibiting":[160],"outstanding":[161],"diagnostic":[162],"capabilities.":[163],"Specific":[164],"metrics":[165],"include":[166],"(97.62%),":[168],"precision":[169],"(98.97%),":[170],"recall":[171],"(96.26%),":[172],"F1":[174],"score":[175],"(97.59%),":[176],"with":[177],"false":[179],"positive":[180],"rate":[181],"only":[183],"1%.":[184],"Finally,":[185],"multiple":[186],"comparative":[187],"experiments":[188],"model's":[191],"significant":[192],"advantages":[193],"tasks.":[197]},"counts_by_year":[],"updated_date":"2026-01-15T23:21:31.212559","created_date":"2026-01-15T00:00:00"}