{"id":"https://openalex.org/W7124191026","doi":"https://doi.org/10.1109/access.2026.3654146","title":"An Explainable Multi-Level Framework for Cervical Cancer Detection Using Traditional Computer Vision and Deep Learning","display_name":"An Explainable Multi-Level Framework for Cervical Cancer Detection Using Traditional Computer Vision and Deep Learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7124191026","doi":"https://doi.org/10.1109/access.2026.3654146"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3654146","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654146","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3654146","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123014304","display_name":"Medha Chawla","orcid":null},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Medha Chawla","raw_affiliation_strings":["Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001921518","display_name":"Pratik Chakraborty","orcid":null},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pratik Chakraborty","raw_affiliation_strings":["Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"raw_orcid":"https://orcid.org/0009-0004-0510-2556","affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123036239","display_name":"Sriram Sunderrajan","orcid":null},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sriram Sunderrajan","raw_affiliation_strings":["Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123038634","display_name":"Rani Oomman Panicker","orcid":null},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rani Oomman Panicker","raw_affiliation_strings":["Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"raw_orcid":"https://orcid.org/0009-0009-4409-9595","affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5123014304"],"corresponding_institution_ids":["https://openalex.org/I164861460"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09350711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"12273","last_page":"12296"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.7559999823570251,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.7559999823570251,"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/T10668","display_name":"Endometrial and Cervical Cancer Treatments","score":0.04179999977350235,"subfield":{"id":"https://openalex.org/subfields/2729","display_name":"Obstetrics and Gynecology"},"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.03720000013709068,"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/interpretability","display_name":"Interpretability","score":0.8988999724388123},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.828000009059906},{"id":"https://openalex.org/keywords/local-binary-patterns","display_name":"Local binary patterns","score":0.7178999781608582},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6832000017166138},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6697999835014343},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6610000133514404},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5792999863624573},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.555899977684021}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8988999724388123},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.828000009059906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7907000184059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896999716758728},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.7178999781608582},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6832000017166138},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6697999835014343},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6610000133514404},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5792999863624573},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.555899977684021},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4984999895095825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43700000643730164},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C2778220009","wikidata":"https://www.wikidata.org/wiki/Q160105","display_name":"Cervical cancer","level":3,"score":0.3578000068664551},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33869999647140503},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.2676999866962433},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2590999901294708}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3654146","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654146","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3654146","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654146","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.7898423671722412,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2035909111","https://openalex.org/W2106813302","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2163352848","https://openalex.org/W2194775991","https://openalex.org/W2618530766","https://openalex.org/W2892046320","https://openalex.org/W2962858109","https://openalex.org/W2996907026","https://openalex.org/W3013098034","https://openalex.org/W3020074006","https://openalex.org/W3027508013","https://openalex.org/W3047113895","https://openalex.org/W3112217930","https://openalex.org/W3118603985","https://openalex.org/W3130039502","https://openalex.org/W3134810602","https://openalex.org/W3200722156","https://openalex.org/W4210510240","https://openalex.org/W4212765645","https://openalex.org/W4221005924","https://openalex.org/W4281887347","https://openalex.org/W4315853183","https://openalex.org/W4365451353","https://openalex.org/W4365814552","https://openalex.org/W4366502847","https://openalex.org/W4377964485","https://openalex.org/W4382931048","https://openalex.org/W4387258831","https://openalex.org/W4387940680","https://openalex.org/W4388671517","https://openalex.org/W4392909071","https://openalex.org/W4394869486","https://openalex.org/W4396834505","https://openalex.org/W4401709300","https://openalex.org/W4402878812","https://openalex.org/W4406886067","https://openalex.org/W4407023520","https://openalex.org/W4407782498","https://openalex.org/W4409473809","https://openalex.org/W4413063176"],"related_works":[],"abstract_inverted_index":{"Cervical":[0],"cancer":[1],"is":[2,28],"one":[3],"of":[4,9,15,25,70,171,227],"the":[5,125,132,138,152,156,161,166,225,228],"most":[6],"common":[7],"causes":[8],"mortality":[10],"among":[11,164],"women":[12],"globally.":[13],"Development":[14],"accurate,":[16],"interpretable":[17],"and":[18,44,60,78,98,109,149,179,193,195,220],"clinically":[19],"deployable":[20],"automated":[21],"systems":[22],"for":[23,52],"detection":[24],"precancerous":[26],"cells":[27],"essential.":[29],"We":[30,47],"propose":[31],"an":[32],"explainable":[33],"multi-level":[34],"framework":[35,229],"that":[36,92],"integrates":[37],"traditional":[38,63],"computer":[39,64],"vision":[40,65],"techniques,":[41],"ML":[42,90,191],"models,":[43,105,167,217],"DL":[45,157,199],"models.":[46,208],"use":[48,102],"three":[49,103,139],"benchmark":[50],"datasets":[51],"evaluating":[53],"our":[54],"framework,":[55],"namely":[56,106],"SIPaKMeD,":[57,144,174],"Mendeley":[58,147,177],"LBC,":[59,148,178],"Herlev.":[61,182],"The":[62,209],"techniques":[66,185],"used":[67],"are":[68,87],"Histogram":[69],"Oriented":[71],"Gradients":[72,197],"(HOG),":[73],"Local":[74],"Binary":[75],"Patterns":[76],"(LBP),":[77],"Scale-Invariant":[79],"Feature":[80,188],"Transform":[81],"with":[82,89,112,128,141],"Bag-of-Visual-Words":[83],"(SIFT-BoVW).":[84],"These":[85],"features":[86,130],"combined":[88],"models":[91],"include":[93],"Logistic":[94],"Regression,":[95],"Random":[96,126],"Forest,":[97],"Linear":[99],"SVM.We":[100],"also":[101],"pretrained":[104],"AlexNet,":[107],"ResNet50,":[108],"EfficientNet-B0,":[110],"along":[111],"a":[113],"custom":[114],"CNN\u2013Transformer.":[115],"Each":[116],"model":[117],"was":[118,212],"evaluated":[119],"across":[120,206,214],"five":[121],"cross-validation":[122],"folds.Within":[123],"theMLbranch,":[124],"Forest":[127],"LBP":[129],"achieved":[131],"highest":[133],"mean":[134,169],"accuracies":[135,170],"on":[136,143,146,151,173,176,181],"all":[137,165,207],"datasets,":[140,219],"72.27%":[142],"80.77%":[145],"47.98%":[150],"Herlev":[153],"dataset.":[154],"Within":[155],"branch,":[158],"ResNet50":[159],"had":[160],"best":[162],"performance":[163],"achieving":[168],"97.55%":[172],"99.48%":[175],"71.53%":[180],"Explainable":[183],"AI":[184],"such":[186],"as":[187],"Importance":[189],"(for":[190,198],"models)":[192,200],"Grad-CAM":[194],"Integrated":[196],"were":[201],"incorporated":[202],"to":[203,223],"ensure":[204],"interpretability":[205],"XAI":[210],"analysis":[211],"structured":[213],"four":[215],"dimensions:":[216],"features,":[218],"lesion":[221],"grades":[222],"evaluate":[224],"effectiveness":[226],"at":[230],"multiple":[231],"levels.":[232]},"counts_by_year":[],"updated_date":"2026-01-29T23:13:10.619473","created_date":"2026-01-15T00:00:00"}