{"id":"https://openalex.org/W6982324526","doi":null,"title":"Hybrid Skin Lesion Detection Integrating CNN and XGBoost for Accurate Diagnosis","display_name":"Hybrid Skin Lesion Detection Integrating CNN and XGBoost for Accurate Diagnosis","publication_year":2024,"publication_date":"2024-10-01","ids":{"openalex":"https://openalex.org/W6982324526"},"language":"en","primary_location":{"id":"pmh:oai:ojs.ijcjournal.org:article/2327","doi":null,"is_oa":true,"landing_page_url":"http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2327","pdf_url":"https://ijcjournal.org/InternationalJournalOfComputer/article/download/2327/868","source":{"id":"https://openalex.org/S4306513923","display_name":"International Journal of Computer (IJC)","issn_l":"2307-4523","issn":["2307-4523"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2307-4523","raw_type":"info:eu-repo/semantics/article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ijcjournal.org/InternationalJournalOfComputer/article/download/2327/868","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Adekunle O. Ajiboye","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I153151563","display_name":"Harrisburg University of Science and Technology","ror":"https://ror.org/02g0s4z48","country_code":"US","type":"education","lineage":["https://openalex.org/I153151563"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Adekunle O. Ajiboye","raw_affiliation_strings":["Harrisburg University of Science and Technology, 326 Market St, Harrisburg, PA, 17101, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harrisburg University of Science and Technology, 326 Market St, Harrisburg, PA, 17101, United States of America","institution_ids":["https://openalex.org/I153151563"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I153151563"],"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":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10862","display_name":"AI in cancer detection","score":0.019200000911951065,"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":0.00039999998989515007,"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/skin-lesion-classification","display_name":"skin lesion classification","score":0.9980000257492065},{"id":"https://openalex.org/keywords/melanoma","display_name":"melanoma","score":0.7720000147819519},{"id":"https://openalex.org/keywords/dermoscopic-images","display_name":"dermoscopic images","score":0.8610000014305115},{"id":"https://openalex.org/keywords/ham10000-dataset","display_name":"HAM10000 dataset","score":0.9279999732971191},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.9789999723434448},{"id":"https://openalex.org/keywords/power-analysis","display_name":"power analysis","score":0.6259999871253967},{"id":"https://openalex.org/keywords/ensemble-classification","display_name":"ensemble classification","score":0.4429999887943268}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7200999855995178},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6700999736785889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6355999708175659},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6151000261306763},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5703999996185303},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5016000270843506},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5011000037193298},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C2988168687","wikidata":"https://www.wikidata.org/wiki/Q949302","display_name":"Skin lesion","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.42160001397132874},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.39640000462532043},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C2777789703","wikidata":"https://www.wikidata.org/wiki/Q192102","display_name":"Skin cancer","level":3,"score":0.288100004196167},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28540000319480896},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2500999867916107},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2409999966621399},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.2378000020980835},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.22869999706745148},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.21490000188350677},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.21379999816417694},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21310000121593475},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.20749999582767487},{"id":"https://openalex.org/C3019497199","wikidata":"https://www.wikidata.org/wiki/Q853516","display_name":"Skin color","level":2,"score":0.20250000059604645},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.20149999856948853},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.19920000433921814},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.1949000060558319},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.19480000436306},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.1932000070810318},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.19249999523162842},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.19110000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:ojs.ijcjournal.org:article/2327","doi":null,"is_oa":true,"landing_page_url":"http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2327","pdf_url":"https://ijcjournal.org/InternationalJournalOfComputer/article/download/2327/868","source":{"id":"https://openalex.org/S4306513923","display_name":"International Journal of Computer (IJC)","issn_l":"2307-4523","issn":["2307-4523"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2307-4523","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:ojs.ijcjournal.org:article/2327","doi":null,"is_oa":true,"landing_page_url":"http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2327","pdf_url":"https://ijcjournal.org/InternationalJournalOfComputer/article/download/2327/868","source":{"id":"https://openalex.org/S4306513923","display_name":"International Journal of Computer (IJC)","issn_l":"2307-4523","issn":["2307-4523"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2307-4523","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.9391000270843506}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310455","display_name":"Kr\u00e6ftens Bek\u00e6mpelse","ror":"https://ror.org/03ytt7k16"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W6982324526.pdf","grobid_xml":"https://content.openalex.org/works/W6982324526.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1966716734","https://openalex.org/W2112796928","https://openalex.org/W2158698691","https://openalex.org/W2194775991","https://openalex.org/W2295598076","https://openalex.org/W2562319768","https://openalex.org/W2581082771","https://openalex.org/W2743072699","https://openalex.org/W2775795276","https://openalex.org/W2794825826","https://openalex.org/W2806853752","https://openalex.org/W2890139949","https://openalex.org/W2919115771","https://openalex.org/W3128947195","https://openalex.org/W4206841660","https://openalex.org/W4307511304","https://openalex.org/W7223740002"],"related_works":["https://openalex.org/W4393140735","https://openalex.org/W4413216191","https://openalex.org/W4406820553","https://openalex.org/W7132840538","https://openalex.org/W4407950464","https://openalex.org/W4392768077","https://openalex.org/W4410078458","https://openalex.org/W7129407534","https://openalex.org/W4409475010","https://openalex.org/W4408057315"],"abstract_inverted_index":{"Skin":[0],"cancer,":[1],"particularly":[2],"melanoma,":[3],"remains":[4],"one":[5],"of":[6,28,55,69,76,113,238,272],"the":[7,38,63,111,141,183,192,220,227,241,245,278],"most":[8],"challenging":[9],"medical":[10,36],"conditions":[11],"due":[12],"to":[13,90,109,131,181,218,319],"its":[14],"rapid":[15],"progression":[16],"and":[17,43,73,84,92,105,135,176,216,256,293,309,330],"high":[18,258],"mortality":[19],"rate":[20],"when":[21],"not":[22],"detected":[23],"early.":[24],"The":[25,138,185,200,223,249,274],"growing":[26],"prevalence":[27],"skin":[29,56,114,150,290],"cancer":[30],"highlights":[31],"a":[32,74,94,204],"significant":[33,283],"problem":[34,112],"in":[35,51,65,288],"diagnostics:":[37],"need":[39],"for":[40,102,107,285,298,313,327],"automated,":[41],"accurate,":[42],"efficient":[44],"classification":[45,108,133,196,233],"systems":[46],"that":[47,226,277],"can":[48],"aid":[49],"dermatologists":[50,287],"diagnosing":[52],"various":[53],"types":[54],"lesions.":[57],"This":[58,87,117],"issue":[59],"is":[60],"exacerbated":[61],"by":[62,162,267],"imbalance":[64,255],"available":[66],"datasets,":[67,305],"underrepresentation":[68],"certain":[70],"lesion":[71,115,151,263,291],"classes,":[72,264],"lack":[75],"generalizable":[77],"diagnostic":[78,295],"tools,":[79],"ultimately":[80],"impacting":[81],"patient":[82,331],"outcomes":[83],"healthcare":[85,322],"efficiency.":[86],"study":[88,139,275],"aimed":[89],"develop":[91],"evaluate":[93,219],"hybrid":[95,229,250,279],"model":[96,136,187,194,230,251,281,311],"integrating":[97],"Convolutional":[98],"Neural":[99],"Networks":[100],"(CNNs)":[101],"feature":[103],"extraction":[104],"XGBoost":[106,193],"address":[110],"classification.":[116],"study's":[118],"guiding":[119],"conceptual":[120],"framework":[121],"was":[122],"applying":[123],"deep":[124],"learning":[125],"techniques":[126],"combined":[127],"with":[128,235],"ensemble":[129],"models":[130],"enhance":[132],"accuracy":[134,237],"interpretability.":[137],"utilized":[140],"HAM10000":[142],"dataset,":[143,243],"comprising":[144],"10,015":[145],"dermatoscopic":[146],"images":[147],"across":[148,261],"seven":[149],"classes.":[152],"Dynamic":[153],"resampling":[154],"based":[155],"on":[156,197,240],"power":[157,260],"analysis":[158],"ensured":[159],"class":[160,254],"balance":[161],"selecting":[163],"158":[164],"samples":[165],"per":[166],"class.":[167],"Image":[168],"preprocessing":[169],"techniques,":[170],"such":[171,210],"as":[172,211,265],"resizing,":[173],"hair":[174],"removal,":[175],"Gaussian":[177],"blurring,":[178],"were":[179],"applied":[180],"standardize":[182],"data.":[184],"CNN":[186,247],"extracted":[188],"hierarchical":[189],"features,":[190],"while":[191],"performed":[195],"these":[198],"features.":[199],"research":[201,300],"methodology":[202],"involved":[203],"quantitative":[205],"approach":[206],"using":[207,303],"performance":[208,234],"metrics":[209],"accuracy,":[212],"precision,":[213],"recall,":[214],"F1-score,":[215],"ROC-AUC":[217,270],"model\u2019s":[221],"effectiveness.":[222],"results":[224],"demonstrated":[225],"CNN-XGBoost":[228,280],"achieved":[231],"superior":[232],"an":[236,268],"86.46%":[239],"test":[242],"outperforming":[244],"standalone":[246],"model.":[248],"effectively":[252],"addressed":[253],"exhibited":[257],"discriminatory":[259],"all":[262],"confirmed":[266],"average":[269],"score":[271],"0.98.":[273],"concludes":[276],"holds":[282],"potential":[284],"assisting":[286],"early":[289],"detection":[292],"improving":[294],"accuracy.":[296],"Recommendations":[297],"future":[299],"include":[301],"validation":[302],"diverse":[304],"incorporating":[306],"clinical":[307],"metadata,":[308],"enhancing":[310],"interpretability":[312],"real-world":[314],"deployment.":[315],"These":[316],"findings":[317],"contribute":[318],"advancing":[320],"AI-driven":[321],"solutions,":[323],"offering":[324],"promising":[325],"implications":[326],"dermatological":[328],"diagnostics":[329],"care.":[332]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
