{"id":"https://openalex.org/W6961947936","doi":"https://doi.org/10.15167/azam-muhammad-adeel_phd2024-02-19","title":"Upper Aero Digestive Tract Cancer Diagnosis using Deep Learning Methods","display_name":"Upper Aero Digestive Tract Cancer Diagnosis using Deep Learning Methods","publication_year":2024,"publication_date":"2024-02-19","ids":{"openalex":"https://openalex.org/W6961947936","doi":"https://doi.org/10.15167/azam-muhammad-adeel_phd2024-02-19"},"language":"en","primary_location":{"id":"pmh:oai:iris.unige.it:11567/1160223","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1160223","pdf_url":"https://unige.iris.cineca.it/bitstream/11567/1160223/1/phdunige_4953500.pdf","source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://unige.iris.cineca.it/bitstream/11567/1160223/1/phdunige_4953500.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"AZAM, MUHAMMAD ADEEL","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"AZAM, MUHAMMAD ADEEL","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T13297","display_name":"History and advancements in chemistry","score":0.10920000076293945,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13297","display_name":"History and advancements in chemistry","score":0.10920000076293945,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10407","display_name":"Lipid Membrane Structure and Behavior","score":0.027400000020861626,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11262","display_name":"Quantum Mechanics and Non-Hermitian Physics","score":0.02539999969303608,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7002999782562256},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.534500002861023},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.47209998965263367},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4490000009536743},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.42989999055862427},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4253000020980835},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4138999879360199},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3743000030517578}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7598000168800354},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7002999782562256},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6694999933242798},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.534500002861023},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.47209998965263367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4253000020980835},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4138999879360199},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3671000003814697},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C2992581678","wikidata":"https://www.wikidata.org/wiki/Q11829360","display_name":"Digestive tract","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3264000117778778},{"id":"https://openalex.org/C17480853","wikidata":"https://www.wikidata.org/wiki/Q5376368","display_name":"Endomicroscopy","level":3,"score":0.3077999949455261},{"id":"https://openalex.org/C3019992690","wikidata":"https://www.wikidata.org/wiki/Q92767510","display_name":"Basal cell","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2971999943256378},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2896000146865845},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:iris.unige.it:11567/1160223","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1160223","pdf_url":"https://unige.iris.cineca.it/bitstream/11567/1160223/1/phdunige_4953500.pdf","source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},{"id":"doi:10.15167/azam-muhammad-adeel_phd2024-02-19","is_oa":true,"landing_page_url":"https://doi.org/10.15167/azam-muhammad-adeel_phd2024-02-19","pdf_url":null,"source":{"id":"https://openalex.org/S7407050993","display_name":"Universit\u00e0 degli Studi di Genova","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:iris.unige.it:11567/1160223","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1160223","pdf_url":"https://unige.iris.cineca.it/bitstream/11567/1160223/1/phdunige_4953500.pdf","source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"sustainable_development_goals":[{"score":0.42666059732437134,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W6961947936.pdf","grobid_xml":"https://content.openalex.org/works/W6961947936.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Objective:":[0],"Narrow":[1],"band":[2],"imaging":[3,22],"(NBI)":[4],"and":[5,68,76,152,190,208,271,292,312,335,374,383,388,399,414,425,452,467,477,479,483,497,524],"white":[6],"light":[7],"(WL)":[8],"are":[9,24],"endoscopic":[10],"techniques":[11,23],"to":[12,60,89,197,205,212,440,445],"visualize":[13],"upper":[14],"aero":[15],"digestive":[16],"tract":[17],"(UADT)":[18],"cancers.":[19],"However,":[20],"these":[21],"less":[25,31],"effective":[26],"for":[27,64,105,125,166,260,361,380,492,511,519,529],"diagnosing":[28],"tumors":[29],"in":[30,53,103,220,411,495,505,533],"competent":[32],"centers":[33],"since":[34],"they":[35],"depend":[36],"on":[37,120,141,241,352],"skilled":[38],"medical":[39],"experts.":[40,252],"Recently,":[41],"there":[42],"has":[43,50],"been":[44],"evidence":[45],"that":[46],"deep":[47],"learning":[48,133],"(DL)":[49],"potential":[51,518],"applications":[52],"UADT":[54,71,127,363],"video":[55,297,499],"endoscopy.":[56],"This":[57,428],"research":[58],"aims":[59],"develop":[61],"a":[62,111,121,178,226,313,488,530],"DL":[63,81,273,406],"the":[65,79,159,214,244,281,362,381,412,441,455,517,527],"automatic":[66],"identification":[67],"delineation":[69,128,245,365],"of":[70,147,162,216,423,443,454],"cancer.&#13;\\nApproach:":[72],"In":[73,280],"both":[74],"WL":[75,496],"NBI":[77,145,390,498],"frames,":[78,243],"YOLO":[80,299],"model":[82,117,124,164,238,360,395,491],"(YOLOv5s":[83,320],"with":[84,144,177,249,267,321,330,419,433],"YOLOv5m)":[85],"ensemble,":[86],"was":[87,138,203,223,239,247,265,355,366],"used":[88,171,204],"diagnose":[90],"laryngeal":[91,435],"squamous":[92],"cell":[93],"carcinoma":[94],"(LSCC).":[95],"Six":[96],"external":[97,142,375,460,471,506],"LSCC":[98,168,210,242,282,296,328,493,534],"laryngoscopy":[99,256,353],"videos":[100,257,354],"were":[101,192,290,293,301,457],"tested":[102,240],"real-time":[104,261],"cancer":[106,199,364],"detection.":[107],"The":[108,156,186,201,237,253,263,317,346,358,393,408,449,470,485],"SegMENT":[109,163,270,359,394,501],"is":[110,158],"segmentation":[112],"convolution":[113],"neural":[114],"networks":[115],"(CNN),":[116],"proposed":[118],"based":[119],"modified":[122,179],"DeepLabV3+":[123],"precise":[126,512],"using":[129,225,303,368],"an":[130,175,304,308],"in-domain":[131],"transfer":[132],"ensemble":[134,318],"technique.":[135],"Its":[136],"accuracy":[137],"further":[139],"validated":[140,259],"datasets":[143,416],"images":[146,436],"oral":[148],"cavity":[149],"SCC":[150,154],"(OSCC)":[151],"oropharyngeal":[153],"(OPSCC).":[155],"SegMENT-Plus":[157,170,222,264,444,515],"improved":[160,417,520],"version":[161],"designed":[165],"large":[167],"datasets.":[169],"EfficientNetB5":[172],"backbone":[173],"as":[174],"encoder":[176],"atrous":[180],"spatial":[181],"pyramid":[182],"pooling":[183],"(m-ASPP)":[184],"block.":[185],"attentions":[187],"blocks":[188],"(SE":[189],"CBAM)":[191],"integrated":[193],"into":[194],"m-ASPP":[195,202],"module":[196],"improve":[198,446],"segmentation.":[200,514],"extract":[206],"local":[207],"global":[209],"features":[211],"overcome":[213],"limitation":[215],"conventional":[217],"ASPP":[218],"modules":[219],"literature.":[221],"evaluated":[224],"multi-center":[227],"dataset":[228],"from":[229,287,377,437,463],"three":[230,250],"hospitals":[231],"(Genoa,":[232],"Brescia,":[233,378,468],"Seoul":[234],"South":[235,465],"Korea).":[236],"performance":[246,451],"compared":[248,266],"otolaryngology":[251],"unseen":[254],"intraoperative":[255],"also":[258],"performance.":[262],"its":[268],"predecessor":[269],"other":[272,405],"models":[274,300],"(UNET,":[275],"ResUNET,":[276],"DeepLabv3+,":[277],"DoubleUET,).&#13;\\nMain":[278],"results:":[279],"detection":[283,329,494,523],"task,":[284],"219":[285,369],"patients":[286,370,432],"Genoa,":[288,438],"Italy":[289,379,439],"enrolled,":[291],"provided":[294],"624":[295],"frames.":[298],"trained":[302],"82.6%":[305],"training":[306],"set,":[307,311],"8.2%":[309],"validation":[310,376,507],"9.2%":[314],"testing":[315,461],"set.":[316],"algorithm":[319,456],"YOLOv5m":[322],"\u2014Test":[323],"Time":[324],"Augmentation)":[325],"achieved":[326,396],"top":[327],"66%":[331],"Precision,":[332],"62%":[333],"Recall,":[334],"63%":[336],"mean":[337],"Average":[338],"Precision":[339],"at":[340],"0.5":[341],"intersection":[342],"over":[343],"union":[344],"(IoU).":[345],"average":[347],"computation":[348],"time":[349],"per":[350],"frame":[351],"0.026":[356],"seconds.":[357],"developed":[367],"(624":[371],"larynx":[372],"frames),":[373],"OPSCC":[382,415],"OCSCC":[384,413],"cohorts":[385,462,472],"involved":[386],"116":[387],"102":[389],"images,":[391],"respectively.":[392,427],"0.68%":[397],"IoU":[398,480],"0.81%":[400],"dice":[401],"coefficient":[402],"(DSC),":[403],"outperforming":[404],"models.":[407],"DSC":[409,421,474],"values":[410,422],"significantly,":[418],"median":[420],"10.3%":[424],"11.9%,":[426],"study":[429,486],"includes":[430],"557":[431],"3933":[434],"development":[442],"LDCC":[447],"delineation.":[448,536],"optimal":[450],"generalization":[453],"confirmed":[458],"by":[459],"Seoul,":[464],"Korea,":[466],"Italy.":[469],"showed":[473],"between":[475,481],"81.4%":[476],"84.9%":[478],"81.8%":[482],"85.7%.&#13;\\nSignificance:":[484],"identified":[487],"suitable":[489],"CNN":[490],"laryngoscopes.":[500],"outperformed":[502],"previous":[503],"results":[504],"cohorts,":[508],"showing":[509],"promise":[510],"tumor":[513,522],"holds":[516],"early":[521],"delineation,":[525],"laying":[526],"foundation":[528],"clinical":[531],"system":[532],"margin":[535]},"counts_by_year":[],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}
