{"id":"https://openalex.org/W7083467584","doi":"https://doi.org/10.5281/zenodo.17208317","title":"MULTI-LEVEL IMAGE DENOISING INTEGRATED MORPHOLOGY-BASED IMAGE QUALITY ENHANCEMENT MODEL WITH EDGE-BASED SEGMENTATION FOR ACCURATE PANCREATIC CANCER DETECTION","display_name":"MULTI-LEVEL IMAGE DENOISING INTEGRATED MORPHOLOGY-BASED IMAGE QUALITY ENHANCEMENT MODEL WITH EDGE-BASED SEGMENTATION FOR ACCURATE PANCREATIC CANCER DETECTION","publication_year":2025,"publication_date":"2025-04-30","ids":{"openalex":"https://openalex.org/W7083467584","doi":"https://doi.org/10.5281/zenodo.17208317"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.17208317","doi":"https://doi.org/10.5281/zenodo.17208317","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17208317","pdf_url":null,"source":{"id":"https://openalex.org/S2764870389","display_name":"Journal of Theoretical and Applied Information Technology","issn_l":"1817-3195","issn":["1817-3195","1992-8645"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","poland-40","russia-white-list-3"],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"volume":null,"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.5281/zenodo.17208317","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Journal of Theoretical and Applied Information Technology","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Journal of Theoretical and Applied Information Technology","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34201615,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.7461000084877014,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.7461000084877014,"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/T10862","display_name":"AI in cancer detection","score":0.11509999632835388,"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/T11019","display_name":"Image Enhancement Techniques","score":0.05979999899864197,"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/pancreatic-cancer-detection","display_name":"pancreatic cancer detection","score":0.9940000176429749},{"id":"https://openalex.org/keywords/ct-images","display_name":"CT images","score":0.7630000114440918},{"id":"https://openalex.org/keywords/wavelet-denoising","display_name":"wavelet denoising","score":0.9110000133514404},{"id":"https://openalex.org/keywords/morphological-operations","display_name":"morphological operations","score":0.7120000123977661},{"id":"https://openalex.org/keywords/image-enhancement","display_name":"image enhancement","score":0.8650000095367432},{"id":"https://openalex.org/keywords/edge-based-segmentation","display_name":"edge-based segmentation","score":0.9470000267028809},{"id":"https://openalex.org/keywords/tumor-segmentation","display_name":"tumor segmentation","score":0.5180000066757202},{"id":"https://openalex.org/keywords/small-nodule-detection","display_name":"small nodule detection","score":0.8460000157356262},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"computer-aided diagnosis","score":0.8960000276565552},{"id":"https://openalex.org/keywords/medical-image-analysis","display_name":"medical image analysis","score":0.7310000061988831},{"id":"https://openalex.org/keywords/contrast-enhancement","display_name":"contrast enhancement","score":0.5889999866485596},{"id":"https://openalex.org/keywords/early-cancer-detection","display_name":"early cancer detection","score":0.3970000147819519},{"id":"https://openalex.org/keywords/low-quality-medical-images","display_name":"low-quality medical images","score":0.6859999895095825},{"id":"https://openalex.org/keywords/cost-effective-diagnostics","display_name":"cost-effective diagnosis","score":0.40400001406669617}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6922000050544739},{"id":"https://openalex.org/C2780210213","wikidata":"https://www.wikidata.org/wiki/Q212961","display_name":"Pancreatic cancer","level":3,"score":0.6690999865531921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5600000023841858},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.5432999730110168},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.516700029373169},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5052000284194946},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5019999742507935},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.4422999918460846},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4262000024318695},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3970000147819519},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3578000068664551},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3564999997615814},{"id":"https://openalex.org/C2985322473","wikidata":"https://www.wikidata.org/wiki/Q3044843","display_name":"Cancer detection","level":3,"score":0.3296000063419342},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.29280000925064087},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.28139999508857727},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.26809999346733093},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C3017601658","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image enhancement","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.17208317","doi":"https://doi.org/10.5281/zenodo.17208317","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17208317","pdf_url":null,"source":{"id":"https://openalex.org/S2764870389","display_name":"Journal of Theoretical and Applied Information Technology","issn_l":"1817-3195","issn":["1817-3195","1992-8645"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","poland-40","russia-white-list-3"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"JournalArticle"},{"id":"doi:10.5281/zenodo.17208318","doi":"https://doi.org/10.5281/zenodo.17208318","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17208318","pdf_url":null,"source":{"id":"https://openalex.org/S2764870389","display_name":"Journal of Theoretical and Applied Information Technology","issn_l":"1817-3195","issn":["1817-3195","1992-8645"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","poland-40","russia-white-list-3"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"JournalArticle"}],"best_oa_location":{"id":"doi:10.5281/zenodo.17208317","doi":"https://doi.org/10.5281/zenodo.17208317","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17208317","pdf_url":null,"source":{"id":"https://openalex.org/S2764870389","display_name":"Journal of Theoretical and Applied Information Technology","issn_l":"1817-3195","issn":["1817-3195","1992-8645"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","poland-40","russia-white-list-3"],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.9476000070571899}],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5841168165206909}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Despite":[0],"the":[1,27,35,41,119,159,169,205,310,322,340,346,362,415,423],"availability":[2],"of":[3,20,37,45,51,87,96,121,126,144,158,208,312,330,348,353,417],"various":[4],"methods,":[5,422],"diagnosing":[6],"and":[7,58,66,75,98,117,153,162,198,218,229,237,251,286,314,350,367,389,406,428,444],"treating":[8],"Pancreatic":[9,179],"Cancer":[10],"(PC)":[11],"continues":[12],"as":[13],"a":[14,210,246,291],"significant":[15],"challenge":[16],"across":[17],"all":[18],"types":[19],"tumours":[21],"with":[22,290,376],"its":[23],"asymptomatic":[24],"development.":[25],"Among":[26],"most":[28],"devastating":[29],"diseases,":[30],"pancreatic":[31,71,88,239,265,436],"cancer":[32,72,89,133,180,240,266,437],"has":[33,130],"claimed":[34],"lives":[36],"countless":[38],"people":[39,175],"around":[40],"globe.":[42],"Traditional":[43],"methods":[44,434],"diagnosis":[46,408],"relied":[47],"on":[48,358],"manual":[49],"analysis":[50,86,185,279],"massive":[52],"datasets,":[53],"which":[54,62,109,171,324,344],"was":[55],"laborious,":[56],"error-prone,":[57],"time-consuming.":[59],"Therefore,":[60],"CADs,":[61],"use":[63,125,143,416],"machine":[64],"learning":[65,68],"deep":[67],"techniques":[69],"for":[70,149,263,296,309,435,449],"denoising,":[73],"segmentation,":[74,439],"classification,":[76],"become":[77],"necessary.":[78],"However,":[79],"there":[80],"are":[81,318,336,372],"obstacles":[82],"to":[83,91,135,167,234,320,400],"medical":[84,122,127,183,221,277],"image":[85,151,184,212,224,278,307,363,438],"owing":[90],"vague":[92],"symptoms,":[93],"high":[94,156],"rates":[95],"misdiagnosis,":[97],"substantial":[99],"monetary":[100],"expenses.":[101],"A":[102],"potential":[103],"answer":[104],"is":[105,186,268,398],"Artificial":[106],"Intelligence":[107],"(AI),":[108],"can":[110,216],"reduce":[111],"patient":[112,412],"expenditures,":[113],"improve":[114,223,321,411],"clinical":[115],"decision-making,":[116],"alleviate":[118],"workload":[120],"workers.":[123],"The":[124,155,301,333,379],"imaging":[128],"scans":[129],"allowed":[131],"many":[132,174],"patients":[134],"detect":[136,238],"anomalies":[137],"earlier":[138],"on.":[139,242],"This":[140,202,270,395],"research":[141,203],"makes":[142,164],"Computed":[145],"Tomography":[146],"(CT)":[147],"images":[148,192],"performing":[150],"denoising":[152,284,308,427],"segmentation.":[154],"price":[157],"required":[160],"equipment":[161],"infrastructure":[163],"it":[165],"difficult":[166],"spread":[168],"technology,":[170],"means":[172],"that":[173,193,215,361,409],"cannot":[176],"afford":[177],"it.":[178],"detection":[181,200,267,342,370],"using":[182,226],"greatly":[187,440],"impeded":[188],"by":[189,260,280,339],"noisy,":[190],"low-quality":[191],"mask":[194],"important":[195,206],"diagnostic":[196,299,442],"details":[197],"lower":[199],"accuracy.":[201,300],"tackles":[204],"problem":[207],"creating":[209],"state-of-the-art":[211,377,433],"processing":[213,421],"method":[214],"reliably":[217],"effectively":[219],"denoise":[220],"images,":[222],"quality":[225],"morphological":[227,316,420],"techniques,":[228,343],"apply":[230],"precise":[231,264],"edge-based":[232,261,419],"segmentation":[233,262,295,365],"better":[235],"visualize":[236],"early":[241,405,450],"In":[243],"this":[244],"research,":[245],"new":[247,447],"Multi-Level":[248,386],"Image":[249,255,387],"Denoising":[250,388],"Integrated":[252],"Morphology":[253],"based":[254,294,305],"Quality":[256],"Enhancement":[257],"Model":[258],"(MIDIMIQEM)":[259],"proposed.":[269],"proposed":[271,380],"model":[272,302,381,431],"solve":[273],"these":[274],"problems":[275],"in":[276,326,385,392],"introducing":[281],"multi":[282],"level":[283],"technique":[285,397],"also":[287],"morphology-based":[288,429],"enhancement":[289,430],"robust":[292],"edge":[293,341],"an":[297,404],"enhanced":[298],"uses":[303],"wavelet":[304],"multilevel":[306],"removal":[311],"noise":[313],"thereafter":[315],"operations":[317],"employed":[319],"contrast":[323],"helps":[325],"easier":[327],"differentiable":[328],"identification":[329],"tumor":[331],"structures.":[332],"segmented":[334],"regions":[335],"further":[337],"improved":[338,374],"increases":[345],"chances":[347],"self":[349],"accurate":[351,407],"determination":[352],"cancerous":[354],"tissues.":[355],"Experimental":[356],"evaluation":[357],"MIDIMIQEM":[359],"demonstrates":[360],"quality,":[364],"accuracy":[366,384,391],"small":[368],"nodules":[369],"performance":[371],"significantly":[373],"compared":[375],"models.":[378],"achieved":[382],"98.7%":[383],"99.3%":[390],"Segmentation":[393],"Accuracy.":[394],"novel":[396],"expected":[399],"help":[401],"radiologists":[402],"make":[403],"could":[410],"outcomes.":[413],"With":[414],"cutting-edge":[418],"suggested":[424],"multi-level":[425],"picture":[426],"outperforms":[432],"enhancing":[441],"precision":[443],"opening":[445],"up":[446],"avenues":[448],"detection.":[451]},"counts_by_year":[],"updated_date":"2026-10-17T09:40:24.721904","created_date":"2025-10-10T00:00:00"}