{"id":"https://openalex.org/W4400129996","doi":"https://doi.org/10.1142/s0219467826500038","title":"Dilated TransUNet + +-Based Segmentation with Multi-Scale Adaptive DenseNet with Bi-LSTM Layer-Aided Prostate Cancer Classification Model","display_name":"Dilated TransUNet + +-Based Segmentation with Multi-Scale Adaptive DenseNet with Bi-LSTM Layer-Aided Prostate Cancer Classification Model","publication_year":2024,"publication_date":"2024-06-27","ids":{"openalex":"https://openalex.org/W4400129996","doi":"https://doi.org/10.1142/s0219467826500038"},"language":"en","primary_location":{"id":"doi:10.1142/s0219467826500038","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219467826500038","pdf_url":null,"source":{"id":"https://openalex.org/S60080701","display_name":"International Journal of Image and Graphics","issn_l":"0219-4678","issn":["0219-4678","1793-6756"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Image and Graphics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099640844","display_name":"Thirupathanna Kurva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thirupathanna Kurva","raw_affiliation_strings":["Department of BME, Government Institute of Electronics, Hyderabad, India","Department of BME, University College of Engineering, Hyderabad 500 007, Telangana, India"],"raw_orcid":"https://orcid.org/0009-0005-3889-6740","affiliations":[{"raw_affiliation_string":"Department of BME, Government Institute of Electronics, Hyderabad, India","institution_ids":[]},{"raw_affiliation_string":"Department of BME, University College of Engineering, Hyderabad 500 007, Telangana, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070604036","display_name":"Malini Mudigonda","orcid":"https://orcid.org/0009-0008-4346-9315"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mudigonda Malini","raw_affiliation_strings":["Department of BME, University College of Engineering, Hyderabad 500 007, Telangana, India","Department of BME, University College of Engineering, Hyderabad 500007, Telangana, India"],"raw_orcid":"https://orcid.org/0009-0008-4346-9315","affiliations":[{"raw_affiliation_string":"Department of BME, University College of Engineering, Hyderabad 500 007, Telangana, India","institution_ids":[]},{"raw_affiliation_string":"Department of BME, University College of Engineering, Hyderabad 500007, Telangana, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3055,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62186491,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"26","issue":"01","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9172000288963318,"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":0.9172000288963318,"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/computer-science","display_name":"Computer science","score":0.7106943726539612},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6644399166107178},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6229829788208008},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5698465704917908},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5634666085243225},{"id":"https://openalex.org/keywords/prostate-cancer","display_name":"Prostate cancer","score":0.5600945353507996},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48509275913238525},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.2812579274177551},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14762607216835022},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.12442880868911743}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7106943726539612},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6644399166107178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6229829788208008},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5698465704917908},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5634666085243225},{"id":"https://openalex.org/C2780192828","wikidata":"https://www.wikidata.org/wiki/Q181257","display_name":"Prostate cancer","level":3,"score":0.5600945353507996},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48509275913238525},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.2812579274177551},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14762607216835022},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.12442880868911743},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0219467826500038","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219467826500038","pdf_url":null,"source":{"id":"https://openalex.org/S60080701","display_name":"International Journal of Image and Graphics","issn_l":"0219-4678","issn":["0219-4678","1793-6756"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Image and Graphics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1993760967","https://openalex.org/W2007611050","https://openalex.org/W2021727929","https://openalex.org/W2030041075","https://openalex.org/W2107030642","https://openalex.org/W2119496927","https://openalex.org/W2139406620","https://openalex.org/W2161194630","https://openalex.org/W2165918841","https://openalex.org/W2323200062","https://openalex.org/W2401040035","https://openalex.org/W2898020899","https://openalex.org/W2945197573","https://openalex.org/W2958620810","https://openalex.org/W2972784366","https://openalex.org/W2977662575","https://openalex.org/W3037100389","https://openalex.org/W3040315387","https://openalex.org/W3042405885","https://openalex.org/W3080805846","https://openalex.org/W3080923371","https://openalex.org/W3109224671","https://openalex.org/W3126165211","https://openalex.org/W3196827207","https://openalex.org/W3203583757","https://openalex.org/W3205007314","https://openalex.org/W4200396787","https://openalex.org/W4205470984","https://openalex.org/W4206918024","https://openalex.org/W4308652025","https://openalex.org/W4309524780","https://openalex.org/W4321093307","https://openalex.org/W4362505800"],"related_works":["https://openalex.org/W299695548","https://openalex.org/W2384122898","https://openalex.org/W151774199","https://openalex.org/W4281885123","https://openalex.org/W2345040638","https://openalex.org/W3171449477","https://openalex.org/W209797606","https://openalex.org/W2087041286","https://openalex.org/W2071753433","https://openalex.org/W3003164792"],"abstract_inverted_index":{"In":[0,24,64,106,249],"common,":[1],"prostate":[2,29,75,146,156,245,252,285],"cancer":[3,39,71,147,157,246,253,286],"is":[4,30,98,138,173,228,238,254],"regarded":[5,99],"as":[6,21,32,100,165,216,230,240],"the":[7,14,22,28,33,43,49,62,68,73,81,92,101,109,117,124,133,141,176,186,211,217,224,231,234,241,244,270,273,278,283],"type":[8,37],"of":[9,38,51,70,104,143,178,192],"cancer,":[10],"which":[11,269],"occurs":[12],"over":[13],"small":[15],"walnut-shaped":[16],"gland":[17,44],"in":[18,48,72,91,119,132,268,272],"men":[19],"termed":[20],"prostate.":[23],"addition":[25],"to":[26,66,85,155,167,184,219,243],"that,":[27],"considered":[31],"most":[34],"generally":[35],"identified":[36],"among":[40],"men.":[41],"Here,":[42,170],"has":[45,55],"been":[46,56],"aided":[47],"production":[50],"seminal":[52],"fluid":[53],"that":[54],"utilized":[57],"for":[58,115,292],"transporting":[59],"and":[60,122,136,149,163,223,288],"nourishing":[61],"sperm.":[63],"order":[65],"exclude":[67],"existence":[69],"tissues,":[74],"biopsy":[76],"techniques":[77,112],"are":[78,113,129,158,197,214,275],"utilized.":[79],"Moreover,":[80,127],"mortality":[82],"rate":[83,298],"due":[84],"this":[86,107,250],"disease":[87,296],"may":[88],"be":[89],"low":[90],"last":[93],"few":[94],"years,":[95],"but":[96],"it":[97,137],"leading":[102],"cause":[103],"cancer.":[105],"case,":[108],"automated":[110],"intelligent":[111],"helpful":[114],"aiding":[116],"pathologists":[118],"minimizing":[120],"fatigue":[121],"enhancing":[123],"routing":[125],"process.":[126],"there":[128],"some":[130],"limitations":[131],"traditional":[134],"model,":[135],"tackled":[139],"with":[140,175,260],"help":[142,177],"a":[144,201],"new":[145],"segmentation":[148,172,287],"classification":[150,247,289,297],"approach.":[151],"Firstly,":[152],"images":[153,213],"related":[154],"attained":[159,229],"from":[160],"standard":[161],"resources":[162],"offered":[164,215],"input":[166,218,242],"lesion":[168,171],"segmentation.":[169],"performed":[174],"Adaptive":[179,258],"Dilated":[180,193],"TransUNet[Formula:":[181,194],"see":[182,195],"text]":[183,196],"get":[185],"segmented":[187,212],"image":[188,227,237],"features.":[189],"The":[190],"parameters":[191,271],"tuned":[198,276],"by":[199,277],"utilizing":[200],"hybrid":[202],"approach":[203,280],"named":[204],"Position-aided":[205],"Pelican-Sea":[206],"Lion":[207],"Optimization":[208],"(PPSLO).":[209],"Then,":[210],"Region-of-Interest":[220],"(ROI)":[221],"cropping,":[222],"ROI":[225,235],"cropped":[226,236],"output.":[232],"Further,":[233],"fed":[239],"phase.":[248],"phase,":[251],"classified":[255],"using":[256],"Multiscale":[257],"DenseNet":[259],"Bi-directional":[261],"Long":[262],"Short":[263],"Term":[264],"Memory":[265],"(MAD-Bi-LSTM)":[266],"Layer,":[267],"network":[274],"developed":[279,284],"PPSLO.":[281],"Hence,":[282],"model":[290],"helps":[291],"securing":[293],"an":[294],"enhanced":[295],"than":[299],"other":[300],"experimental":[301],"observations.":[302]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
