{"id":"https://openalex.org/W4385068854","doi":"https://doi.org/10.3233/web-230042","title":"Liver cancer classification via deep hybrid model from CT image with improved texture feature set and fuzzy clustering based segmentation","display_name":"Liver cancer classification via deep hybrid model from CT image with improved texture feature set and fuzzy clustering based segmentation","publication_year":2023,"publication_date":"2023-07-21","ids":{"openalex":"https://openalex.org/W4385068854","doi":"https://doi.org/10.3233/web-230042"},"language":"en","primary_location":{"id":"doi:10.3233/web-230042","is_oa":false,"landing_page_url":"https://doi.org/10.3233/web-230042","pdf_url":null,"source":{"id":"https://openalex.org/S4210183871","display_name":"Web Intelligence","issn_l":"2405-6456","issn":["2405-6456","2405-6464"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Web Intelligence","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/A5109624616","display_name":"V. Tejaswi","orcid":null},"institutions":[{"id":"https://openalex.org/I875944469","display_name":"Koneru Lakshmaiah Education Foundation","ror":"https://ror.org/02k949197","country_code":"IN","type":"education","lineage":["https://openalex.org/I875944469"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vinnakota Sai Durga Tejaswi","raw_affiliation_strings":["Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, 522302, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, 522302, India","institution_ids":["https://openalex.org/I875944469"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000984203","display_name":"Venubabu Rachapudi","orcid":"https://orcid.org/0000-0002-5969-7733"},"institutions":[{"id":"https://openalex.org/I875944469","display_name":"Koneru Lakshmaiah Education Foundation","ror":"https://ror.org/02k949197","country_code":"IN","type":"education","lineage":["https://openalex.org/I875944469"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venubabu Rachapudi","raw_affiliation_strings":["Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, 522302, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, 522302, India","institution_ids":["https://openalex.org/I875944469"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5109624616"],"corresponding_institution_ids":["https://openalex.org/I875944469"],"apc_list":null,"apc_paid":null,"fwci":0.2325,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55017601,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"22","issue":"3","first_page":"291","last_page":"314"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9587000012397766,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9526000022888184,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8283157348632812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7428655624389648},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6558105945587158},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5501257181167603},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5494073629379272},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5413287878036499},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5172763466835022},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.48721736669540405},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4350922107696533},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34339505434036255}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8283157348632812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7428655624389648},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6558105945587158},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5501257181167603},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5494073629379272},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5413287878036499},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5172763466835022},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.48721736669540405},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4350922107696533},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34339505434036255},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/web-230042","is_oa":false,"landing_page_url":"https://doi.org/10.3233/web-230042","pdf_url":null,"source":{"id":"https://openalex.org/S4210183871","display_name":"Web Intelligence","issn_l":"2405-6456","issn":["2405-6456","2405-6464"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Web Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1968419113","https://openalex.org/W2009659028","https://openalex.org/W2912983208","https://openalex.org/W2982562138","https://openalex.org/W2997741452","https://openalex.org/W3007206105","https://openalex.org/W3038476345","https://openalex.org/W3080229624","https://openalex.org/W3090097219","https://openalex.org/W3094031523","https://openalex.org/W3118839239","https://openalex.org/W3122474843","https://openalex.org/W3126228994","https://openalex.org/W3128987019","https://openalex.org/W3135107870","https://openalex.org/W3159267473","https://openalex.org/W3162548489","https://openalex.org/W3164525109","https://openalex.org/W3168104563","https://openalex.org/W3178858981","https://openalex.org/W3187719629","https://openalex.org/W3187978113","https://openalex.org/W3190546449","https://openalex.org/W3201218156","https://openalex.org/W4210749369","https://openalex.org/W4220751358","https://openalex.org/W4281666884","https://openalex.org/W4286208787","https://openalex.org/W4289779309"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2804364458","https://openalex.org/W4298130764","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W2132641928","https://openalex.org/W2090259340","https://openalex.org/W4310225030","https://openalex.org/W2083665254","https://openalex.org/W1522196789"],"abstract_inverted_index":{"One":[0],"of":[1,5,48,74,129,172,206,216,224],"the":[2,15,19,29,38,46,85,88,96,106,122,127,130,135,159,165,169,189,197,204,207,213,222],"leading":[3],"causes":[4],"death":[6],"for":[7,64,95,118,175,195],"people":[8],"worldwide":[9],"is":[10,22,178,227],"liver":[11,49,65],"cancer.":[12],"Manually":[13],"identifying":[14],"cancer":[16,66],"tissue":[17],"in":[18,51],"current":[20],"situation":[21],"a":[23,61],"challenging":[24],"and":[25,36,82,151,180,242],"time-consuming":[26],"task.":[27],"Assessing":[28],"tumor":[30],"load,":[31],"planning":[32],"therapies,":[33],"making":[34],"predictions,":[35],"tracking":[37],"clinical":[39],"response":[40],"can":[41],"all":[42],"be":[43,92,103,116,126],"done":[44],"using":[45],"segmentation":[47,107],"lesions":[50],"Computed":[52],"Tomography":[53],"(CT)":[54],"scans.":[55],"In":[56,84,183],"this":[57,184],"paper":[58],"we":[59,186,201],"propose":[60],"new":[62],"technique":[63,114],"classification":[67,166,176],"with":[68],"CT":[69],"image.":[70,208],"This":[71,99,209],"method":[72],"consists":[73],"four":[75],"stages":[76],"like":[77],"pre-processing,":[78],"segmentation,":[79],"feature":[80,131],"extraction":[81,132],"classification.":[83],"initial":[86],"stage":[87],"input":[89,128],"image":[90,119,124],"will":[91,102,115,125,187,202],"pre":[93],"processed":[94],"quality":[97,205],"enhancement.":[98],"preprocessed":[100],"output":[101],"subjected":[104,163],"to":[105,164],"phase;":[108],"here":[109,168],"improved":[110],"deep":[111],"fuzzy":[112],"clustering":[113],"applied":[117],"segmentation.":[120],"Subsequently,":[121],"segmented":[123],"phase,":[133],"where":[134],"extracted":[136,160],"features":[137,150,161],"are":[138,162],"named":[139],"as":[140],"Improved":[141],"Gabor":[142],"Transitional":[143],"Pattern,":[144],"Grey-Level":[145],"Co-occurrence":[146],"Matrix":[147],"(GLCM),":[148],"Statistical":[149],"Convolutional":[152],"Neural":[153],"Network":[154],"(CNN)":[155],"based":[156],"feature.":[157],"Finally":[158],"stage,":[167],"two":[170],"types":[171],"classifiers":[173],"used":[174],"that":[177,200],"Bi-GRU":[179],"Deep":[181],"Maxout.":[182],"phase":[185],"apply":[188],"Crossover":[190],"mutated":[191],"COOT":[192,243],"optimization":[193],"(CMCO)":[194],"tuning":[196],"weights,":[198],"So":[199],"improve":[203],"proposed":[210],"technique,":[211],"present":[212],"best":[214],"accuracy":[215,223],"disease":[217],"identification.":[218],"The":[219],"CMCO":[220],"gained":[221],"95.58%,":[225],"which":[226],"preferable":[228],"than":[229],"AO":[230],"=":[231,234,237,240,244],"92.16%,":[232],"COA":[233],"89.38%,":[235],"TSA":[236],"88.05%,":[238],"AOA":[239],"92.05%":[241],"91.95%,":[245],"respectively.":[246]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}