{"id":"https://openalex.org/W7213257534","doi":"https://doi.org/10.17694/bajece.1871168","title":"YOLO\u2013SAM-Guided ROI-Based Deep Learning for Non-Invasive Neonatal Jaundice Detection","display_name":"YOLO\u2013SAM-Guided ROI-Based Deep Learning for Non-Invasive Neonatal Jaundice Detection","publication_year":2026,"publication_date":"2026-09-16","ids":{"openalex":"https://openalex.org/W7213257534","doi":"https://doi.org/10.17694/bajece.1871168"},"language":"en","primary_location":{"id":"doi:10.17694/bajece.1871168","doi":"https://doi.org/10.17694/bajece.1871168","is_oa":true,"landing_page_url":"https://doi.org/10.17694/bajece.1871168","pdf_url":"https://dergipark.org.tr/en/download/article-file/5639004","source":{"id":"https://openalex.org/S4210187447","display_name":"Balkan Journal of Electrical and Computer Engineering","issn_l":"2147-284X","issn":["2147-284X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Balkan Journal of Electrical and Computer Engineering","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://dergipark.org.tr/en/download/article-file/5639004","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026215400","display_name":"Busra Aslan","orcid":"https://orcid.org/0000-0002-0313-0172","observed_orcids":["https://orcid.org/0000-0002-0313-0172"]},"institutions":[{"id":"https://openalex.org/I4210117254","display_name":"Konya Technical University","ror":"https://ror.org/02s82rs08","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210117254"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"B\u00fc\u015fra Aslan","raw_affiliation_strings":["KONYA TEKN\u0130K \u00dcN\u0130VERS\u0130TES\u0130"],"raw_orcid":"https://orcid.org/0000-0002-0313-0172","affiliations":[{"raw_affiliation_string":"KONYA TEKN\u0130K \u00dcN\u0130VERS\u0130TES\u0130","institution_ids":["https://openalex.org/I4210117254"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5026215400"],"corresponding_institution_ids":["https://openalex.org/I4210117254"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12068","display_name":"Neonatal Health and Biochemistry","score":0.5967000126838684,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T12068","display_name":"Neonatal Health and Biochemistry","score":0.5967000126838684,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.21310000121593475,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.040800001472234726,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/yolo","display_name":"YOLO","score":0.6880000233650208},{"id":"https://openalex.org/keywords/segment-anything-model","display_name":"Segment Anything Model","score":0.9990000128746033},{"id":"https://openalex.org/keywords/roi-extraction","display_name":"region of interest extraction","score":0.9290000200271606},{"id":"https://openalex.org/keywords/hybrid-cnn-lstm-model","display_name":"CNN-LSTM hybrid model","score":0.753000020980835},{"id":"https://openalex.org/keywords/resnet-18","display_name":"ResNet-18","score":0.7139999866485596},{"id":"https://openalex.org/keywords/mobilenetv2","display_name":"MobileNetV2","score":0.9929999709129333},{"id":"https://openalex.org/keywords/bilirubin","display_name":"bilirubin","score":0.7749999761581421},{"id":"https://openalex.org/keywords/non-invasive-diagnosis","display_name":"non-invasive diagnosis","score":0.5479999780654907},{"id":"https://openalex.org/keywords/resource-limited-settings","display_name":"resource-limited settings","score":0.5809999704360962}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8032000064849854},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.708899974822998},{"id":"https://openalex.org/C2778456037","wikidata":"https://www.wikidata.org/wiki/Q133244","display_name":"Jaundice","level":2,"score":0.6962000131607056},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6773999929428101},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5238999724388123},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5090000033378601},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4140999913215637},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.3635999858379364},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3628999888896942},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.34540000557899475},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.31459999084472656},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.30979999899864197},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2892000079154968},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2590999901294708},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.17694/bajece.1871168","doi":"https://doi.org/10.17694/bajece.1871168","is_oa":true,"landing_page_url":"https://doi.org/10.17694/bajece.1871168","pdf_url":"https://dergipark.org.tr/en/download/article-file/5639004","source":{"id":"https://openalex.org/S4210187447","display_name":"Balkan Journal of Electrical and Computer Engineering","issn_l":"2147-284X","issn":["2147-284X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Balkan Journal of Electrical and Computer Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.17694/bajece.1871168","doi":"https://doi.org/10.17694/bajece.1871168","is_oa":true,"landing_page_url":"https://doi.org/10.17694/bajece.1871168","pdf_url":"https://dergipark.org.tr/en/download/article-file/5639004","source":{"id":"https://openalex.org/S4210187447","display_name":"Balkan Journal of Electrical and Computer Engineering","issn_l":"2147-284X","issn":["2147-284X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Balkan Journal of Electrical and Computer Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8999999761581421}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7213257534.pdf","grobid_xml":"https://content.openalex.org/works/W7213257534.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W2072831512","https://openalex.org/W2194775991","https://openalex.org/W2612445135","https://openalex.org/W2797106542","https://openalex.org/W4313622600","https://openalex.org/W4386011130","https://openalex.org/W4386392961","https://openalex.org/W4388832744","https://openalex.org/W4389048137","https://openalex.org/W4391034169","https://openalex.org/W4392162056","https://openalex.org/W4394322811","https://openalex.org/W4404594502","https://openalex.org/W4407157026","https://openalex.org/W4409174745","https://openalex.org/W4410078458","https://openalex.org/W4412416995","https://openalex.org/W4415240047","https://openalex.org/W4415247129","https://openalex.org/W4415341811","https://openalex.org/W4416389720"],"related_works":["https://openalex.org/W7117787921","https://openalex.org/W7220782262","https://openalex.org/W6982438210","https://openalex.org/W7130730465","https://openalex.org/W7125809715","https://openalex.org/W7129102277","https://openalex.org/W7163049069","https://openalex.org/W4402943611","https://openalex.org/W3134747610","https://openalex.org/W4392162056"],"abstract_inverted_index":{"Early":[0],"and":[1,27,33,76,99,108,164,179,214,226],"reliable":[2,213],"detection":[3,50,221],"of":[4,8,51,86,130,140,153],"neonatal":[5,52,167,219],"jaundice":[6,53,168,220],"is":[7,145],"critical":[9],"importance":[10],"for":[11,47,217],"preventing":[12],"bilirubin-induced":[13],"neurological":[14],"damage.":[15],"However,":[16],"visual":[17],"assessment":[18],"methods":[19],"widely":[20],"used":[21],"in":[22,224],"clinical":[23,228],"practice":[24],"are":[25],"subjective":[26],"highly":[28],"dependent":[29],"on":[30,70,158],"the":[31,48,55,59,74,80,100,106,116,128,138,146,151,182,187],"examiner":[32],"environmental":[34],"conditions.":[35],"In":[36,58,79,105],"this":[37,143],"study,":[38],"three":[39],"different":[40],"deep":[41],"learning\u2013based":[42],"approaches":[43],"were":[44,118],"systematically":[45,149],"evaluated":[46],"automatic":[49,83,155],"using":[54,73,95,161],"NeoJaundice":[56],"dataset.":[57,169],"first":[60,147],"approach,":[61,82,111],"direct":[62],"Convolutional":[63],"Neural":[64],"Network":[65],"(CNN)-based":[66],"classification":[67,94,159],"was":[68,90],"performed":[69],"raw":[71],"images":[72],"ResNet18":[75,195],"MobileNetV2":[77],"architectures.":[78],"second":[81],"pixel-level":[84],"region":[85],"interest":[87],"(ROI)":[88],"extraction":[89,157,205],"applied":[91],"prior":[92],"to":[93,120,148],"YOLO-based":[96],"coarse":[97],"localization":[98],"Segment":[101],"Anything":[102],"Model":[103],"(SAM).":[104],"third":[107],"most":[109],"advanced":[110],"CNN":[112],"features":[113],"ex-tracted":[114],"from":[115],"ROI":[117,156,204],"transferred":[119],"an":[121],"Long":[122],"Short-Term":[123],"Memory":[124],"(LSTM)":[125],"network":[126],"with":[127,194,206],"aim":[129],"modeling":[131,209],"contextual":[132,207],"dependencies":[133],"between":[134],"spatial":[135],"representations.":[136],"To":[137],"best":[139],"our":[141],"knowledge,":[142],"study":[144],"examine":[150],"effect":[152],"YOLO\u2013SAM\u2013based":[154],"performance":[160],"a":[162,211],"color-sensitive":[163],"clinically":[165],"labeled":[166],"Experimental":[170],"results":[171],"show":[172],"that":[173,201],"ROI-based":[174],"models":[175],"improve":[176],"inter-class":[177],"balance":[178],"sensitivity,":[180],"while":[181],"hybrid":[183],"CNN\u2013LSTM":[184],"architecture":[185],"achieves":[186],"highest":[188],"overall":[189],"accuracy":[190],"(76.45%)":[191],"when":[192],"combined":[193],"features.":[196],"The":[197],"obtained":[198],"findings":[199],"demonstrate":[200],"combining":[202],"precise":[203],"feature":[208],"provides":[210],"more":[212],"generalizable":[215],"framework":[216],"camera-based":[218],"systems,":[222],"particularly":[223],"low-resource":[225],"non-invasive":[227],"screening":[229],"scenarios.":[230]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-09-16T00:00:00"}
