{"id":"https://openalex.org/W4400824966","doi":"https://doi.org/10.1142/s0219691324500395","title":"Heuristic strategy using hybrid deep learning with transfer learning for oral cancer detection","display_name":"Heuristic strategy using hybrid deep learning with transfer learning for oral cancer detection","publication_year":2024,"publication_date":"2024-07-19","ids":{"openalex":"https://openalex.org/W4400824966","doi":"https://doi.org/10.1142/s0219691324500395"},"language":"en","primary_location":{"id":"doi:10.1142/s0219691324500395","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219691324500395","pdf_url":null,"source":{"id":"https://openalex.org/S56986848","display_name":"International Journal of Wavelets Multiresolution and Information Processing","issn_l":"0219-6913","issn":["0219-6913","1793-690X"],"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 Wavelets, Multiresolution and Information Processing","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/A5013488038","display_name":"T. Saraswathi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"T. Saraswathi","raw_affiliation_strings":["Information Technology, Easwari Engineering College (Autonomous), Ramapuram, Chennai, Tamil Nadu 600089, India"],"raw_orcid":"https://orcid.org/0000-0003-1095-9015","affiliations":[{"raw_affiliation_string":"Information Technology, Easwari Engineering College (Autonomous), Ramapuram, Chennai, Tamil Nadu 600089, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087026028","display_name":"V. Murali Bhaskaran","orcid":null},"institutions":[{"id":"https://openalex.org/I4399657946","display_name":"Rajalakshmi Engineering College","ror":"https://ror.org/01dw2vm55","country_code":null,"type":"education","lineage":["https://openalex.org/I4399657946"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"V. Murali Bhaskaran","raw_affiliation_strings":["Computer Science and Engineering, Rajalakshmi Engineering College, Thandalam, Mevalurkuppam, Tamil Nadu 602105, India"],"raw_orcid":"https://orcid.org/0000-0001-6114-4305","affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Rajalakshmi Engineering College, Thandalam, Mevalurkuppam, Tamil Nadu 602105, India","institution_ids":["https://openalex.org/I4399657946"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5013488038"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7499,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.90301116,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"23","issue":"01","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10307","display_name":"Head and Neck Cancer Studies","score":0.9412999749183655,"subfield":{"id":"https://openalex.org/subfields/2733","display_name":"Otorhinolaryngology"},"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/T10307","display_name":"Head and Neck Cancer Studies","score":0.9412999749183655,"subfield":{"id":"https://openalex.org/subfields/2733","display_name":"Otorhinolaryngology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7579737305641174},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.6341037750244141},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6060445308685303},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5824557542800903},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5427975058555603},{"id":"https://openalex.org/keywords/hybrid-learning","display_name":"Hybrid learning","score":0.4977874755859375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4909382164478302},{"id":"https://openalex.org/keywords/cancer-detection","display_name":"Cancer detection","score":0.48118382692337036},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.3575182557106018},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15920576453208923},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.06629768013954163}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7579737305641174},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.6341037750244141},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6060445308685303},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5824557542800903},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5427975058555603},{"id":"https://openalex.org/C3018790387","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Hybrid learning","level":2,"score":0.4977874755859375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4909382164478302},{"id":"https://openalex.org/C2985322473","wikidata":"https://www.wikidata.org/wiki/Q3044843","display_name":"Cancer detection","level":3,"score":0.48118382692337036},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.3575182557106018},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15920576453208923},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.06629768013954163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0219691324500395","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219691324500395","pdf_url":null,"source":{"id":"https://openalex.org/S56986848","display_name":"International Journal of Wavelets Multiresolution and Information Processing","issn_l":"0219-6913","issn":["0219-6913","1793-690X"],"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 Wavelets, Multiresolution and Information Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1578299346","https://openalex.org/W1608714496","https://openalex.org/W1965017639","https://openalex.org/W1966178139","https://openalex.org/W1995959877","https://openalex.org/W2006746526","https://openalex.org/W2024309008","https://openalex.org/W2039735516","https://openalex.org/W2061438946","https://openalex.org/W2108847016","https://openalex.org/W2118603391","https://openalex.org/W2376894730","https://openalex.org/W2574399626","https://openalex.org/W2620915497","https://openalex.org/W2791358713","https://openalex.org/W2890460335","https://openalex.org/W2896760986","https://openalex.org/W2907632336","https://openalex.org/W2932491249","https://openalex.org/W2942342059","https://openalex.org/W2945414752","https://openalex.org/W2973255325","https://openalex.org/W2984191725","https://openalex.org/W3022202626","https://openalex.org/W3024740627","https://openalex.org/W3027764156","https://openalex.org/W3044073403","https://openalex.org/W3046285857","https://openalex.org/W3090253870","https://openalex.org/W3090799123","https://openalex.org/W3094967083","https://openalex.org/W3107263686","https://openalex.org/W3122837895","https://openalex.org/W3139868056","https://openalex.org/W3153007432","https://openalex.org/W3186297450","https://openalex.org/W4200359278","https://openalex.org/W4220845045","https://openalex.org/W4220989242","https://openalex.org/W4225769600","https://openalex.org/W4281788556","https://openalex.org/W4281903125","https://openalex.org/W4291226689"],"related_works":["https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W3192840557","https://openalex.org/W2951211570","https://openalex.org/W4375928479","https://openalex.org/W3167935049","https://openalex.org/W3023427754","https://openalex.org/W3131673289","https://openalex.org/W4393011546","https://openalex.org/W3198847674"],"abstract_inverted_index":{"Oral":[0,16],"cancer":[1,17,250],"becomes":[2,28],"the":[3,9,14,20,24,48,105,125,133,139,144,148,151,165,174,188,192,198,202,208,213,216,241,256],"most":[4],"disastrous":[5],"ailment":[6],"that":[7],"affects":[8],"oral":[10,35,54,249],"cavity":[11],"parts":[12],"of":[13,33,50,150],"mouth.":[15],"diagnosis":[18],"is":[19,80,98,107,130,179],"main":[21],"challenge":[22],"in":[23,123],"medical":[25],"field.":[26],"It":[27],"expensive":[29],"and":[30,45,52,67,88,159,233],"less":[31],"capable":[32],"classifying":[34],"cancer.":[36],"In":[37,187],"some":[38],"cases,":[39],"it":[40],"may":[41],"cause":[42],"unnecessary":[43],"morbidity":[44],"mortality.":[46],"Recently,":[47],"detection":[49],"malignant":[51],"premalignant":[53],"lesions":[55],"has":[56,219],"been":[57],"a":[58,72,110],"critical":[59],"process":[60],"owing":[61],"to":[62,100,142,206,236],"their":[63],"low":[64],"image":[65],"resolution":[66],"lower":[68],"acquisition":[69],"time.":[70],"Thus,":[71,240],"novel":[73],"hybrid":[74],"deep":[75,113],"learning":[76,114,156],"with":[77,132,181,251],"meta-heuristic-based":[78],"optimization":[79,199],"proposed.":[81],"The":[82,95],"pre-processing":[83],"occurs":[84],"by":[85,109,197],"median":[86],"filtering":[87],"Contrast":[89],"Limited":[90],"Adaptive":[91],"Histogram":[92],"Equalization":[93],"(CLAHE).":[94],"CLAHE":[96],"method":[97],"utilized":[99],"reduce":[101],"unwanted":[102],"noise.":[103],"Finally,":[104],"classification":[106,253],"done":[108],"proposed":[111,166,190,242],"hybrid-based":[112],"model":[115,218,243],"termed":[116],"as":[117,155],"Recurrent":[118,134],"Deep":[119,126],"Belief":[120,127],"Network":[121,128,136],"(RDBN),":[122],"which":[124],"(DBN)":[129],"incorporated":[131],"Neural":[135],"(RNN).":[137],"Here,":[138,201],"RDBN":[140,152],"helps":[141],"increase":[143],"performance":[145,254],"classification.":[146],"Furthermore,":[147],"hyperparameters":[149],"model,":[153,191],"such":[154],"rate,":[157],"epochs":[158],"hidden":[160],"neurons,":[161],"are":[162,195,204],"tuned":[163],"using":[164],"Hybrid":[167],"Beetle-Barnacle":[168],"Swarm":[169,183],"Optimization":[170,184],"(HBBSO)":[171],"algorithm,":[172],"where":[173],"Barnacles":[175],"Mating":[176],"Optimizer":[177],"(BMO)":[178],"superimposed":[180],"Beetle":[182],"(BSO)":[185],"algorithms.":[186,200],"given":[189],"selected":[193],"features":[194],"extracted":[196],"parameters":[203],"fine-tuned":[205],"get":[207],"better":[209,222],"optimal":[210],"solution.":[211],"From":[212],"experimental":[214],"outcome,":[215],"developed":[217],"acquired":[220],"5.2%":[221],"than":[223,227,231,255],"PSO-RDBN,":[224],"5.6%":[225],"improved":[226],"GWO-RDBN,":[228],"3.2%":[229],"enhanced":[230,252],"BSO-RDBN":[232],"3.5%":[234],"superior":[235],"BMO-RDBN":[237],"regarding":[238],"accuracy.":[239],"achieves":[244],"higher":[245],"results":[246],"for":[247],"detecting":[248],"existing":[257],"approaches.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
