{"id":"https://openalex.org/W4285731970","doi":"https://doi.org/10.1080/21681163.2022.2099300","title":"An automated liver tumour segmentation and classification model by deep learning based approaches","display_name":"An automated liver tumour segmentation and classification model by deep learning based approaches","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4285731970","doi":"https://doi.org/10.1080/21681163.2022.2099300"},"language":"en","primary_location":{"id":"doi:10.1080/21681163.2022.2099300","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2022.2099300","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5089554894","display_name":"Sayan Saha Roy","orcid":"https://orcid.org/0000-0002-1245-8161"},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sayan Saha Roy","raw_affiliation_strings":["Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India"],"raw_orcid":"https://orcid.org/0000-0002-1245-8161","affiliations":[{"raw_affiliation_string":"Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India","institution_ids":["https://openalex.org/I106542073"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005500537","display_name":"Shraban Roy","orcid":null},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shraban Roy","raw_affiliation_strings":["Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India","institution_ids":["https://openalex.org/I106542073"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101668453","display_name":"Prithwijit Mukherjee","orcid":"https://orcid.org/0000-0003-4898-7265"},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Prithwijit Mukherjee","raw_affiliation_strings":["Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India","institution_ids":["https://openalex.org/I106542073"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101305671","display_name":"Anisha Halder Roy","orcid":"https://orcid.org/0009-0001-0080-7993"},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anisha Halder Roy","raw_affiliation_strings":["Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India","institution_ids":["https://openalex.org/I106542073"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101668453"],"corresponding_institution_ids":["https://openalex.org/I106542073"],"apc_list":null,"apc_paid":null,"fwci":3.7343,"has_fulltext":false,"cited_by_count":52,"citation_normalized_percentile":{"value":0.94358081,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"11","issue":"3","first_page":"638","last_page":"650"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9988999962806702,"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":0.9988999962806702,"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/T10862","display_name":"AI in cancer detection","score":0.9975000023841858,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7828489542007446},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7185671329498291},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.705080509185791},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6746060848236084},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6044123768806458},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5203635096549988},{"id":"https://openalex.org/keywords/liver-cancer","display_name":"Liver cancer","score":0.5076450109481812},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4779893755912781},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.42056310176849365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35742294788360596},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.24902114272117615},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.18093115091323853}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7828489542007446},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7185671329498291},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.705080509185791},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6746060848236084},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6044123768806458},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5203635096549988},{"id":"https://openalex.org/C2776231280","wikidata":"https://www.wikidata.org/wiki/Q623031","display_name":"Liver cancer","level":3,"score":0.5076450109481812},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4779893755912781},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.42056310176849365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35742294788360596},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.24902114272117615},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.18093115091323853},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/21681163.2022.2099300","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2022.2099300","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1865761","https://openalex.org/W855272188","https://openalex.org/W1884191083","https://openalex.org/W1915761309","https://openalex.org/W1980911747","https://openalex.org/W1996668601","https://openalex.org/W2042597321","https://openalex.org/W2051477566","https://openalex.org/W2062805131","https://openalex.org/W2204983183","https://openalex.org/W2205638955","https://openalex.org/W2330062677","https://openalex.org/W2357815549","https://openalex.org/W2463818697","https://openalex.org/W2499308752","https://openalex.org/W2526009326","https://openalex.org/W2594097440","https://openalex.org/W2600979969","https://openalex.org/W2608353599","https://openalex.org/W2626569079","https://openalex.org/W2762067889","https://openalex.org/W2765571304","https://openalex.org/W2790726660","https://openalex.org/W2792023360","https://openalex.org/W2794026873","https://openalex.org/W2940773812","https://openalex.org/W2949721846","https://openalex.org/W2954996726","https://openalex.org/W2964227007","https://openalex.org/W2972594929","https://openalex.org/W3033721673","https://openalex.org/W3038476345","https://openalex.org/W3098843902","https://openalex.org/W3100523627","https://openalex.org/W3108981504","https://openalex.org/W3190516691","https://openalex.org/W4205099665","https://openalex.org/W4210278342","https://openalex.org/W4236296094","https://openalex.org/W4280559724","https://openalex.org/W4281666884","https://openalex.org/W4281878336","https://openalex.org/W6948015715"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4315434538","https://openalex.org/W4206989953","https://openalex.org/W2072289174"],"abstract_inverted_index":{"Liver":[0],"cancer":[1,14],"is":[2,70,168,177],"regarded":[3],"as":[4],"one":[5],"of":[6,13,48,96,107,159,165,179,237,241,252],"the":[7,17,32,59,108,136,150,153,160,186,191,199,253],"most":[8],"common":[9],"and":[10,23,58,83,111,146,194,197,229,262],"leading":[11],"causes":[12],"death":[15],"around":[16],"world.":[18],"Automatic":[19],"liver":[20,68,80,93,109,217,254,270],"tumour":[21,33,65,81,118,260],"segmentation":[22,82,106,139,193,251],"classification":[24,41,84,154,158],"techniques":[25],"are":[26,51],"essential":[27],"for":[28,91,202,250],"assisting":[29],"doctors":[30],"in":[31,64,86,149,245],"diagnosis":[34],"process.":[35],"As":[36],"artificial":[37],"intelligence":[38],"progresses,":[39],"powerful":[40],"algorithms":[42],"that":[43,176],"can":[44],"modify":[45],"a":[46,71,77,123,207,247,264],"variety":[47],"real-world":[49],"applications":[50],"becoming":[52],"accessible.":[53],"Due":[54],"to":[55,134,142,169,258,268],"noise,":[56],"non-homogeneity":[57],"considerable":[60],"appearance":[61],"variability":[62],"seen":[63],"tissue,":[66],"classifying":[67,195],"tumours":[69,94,161,218],"difficult":[72],"undertaking.":[73],"We":[74,120],"have":[75,121],"offered":[76],"new":[78],"automatic":[79],"methodology":[85],"this":[87,166,242],"paper.":[88],"The":[89,138,163,239],"procedure":[90],"detecting":[92],"consists":[95],"two":[97],"steps:":[98],"first,":[99],"mask-RCNN":[100],"(Regions":[101],"with":[102,172,233],"Convolutional":[103],"Neural":[104,126],"Networks)":[105],"part":[110],"then":[112],"MSER":[113,257],"(Maximally":[114],"Stable":[115],"Extremal":[116],"Regions)":[117],"identification.":[119],"used":[122],"hybrid":[124,265],"Convolution":[125],"Network":[127],"(CNN)":[128],"model":[129],"based":[130],"on":[131,185],"deep":[132],"learning":[133],"perform":[135],"classification.":[137],"framework":[140],"attempts":[141],"distinguish":[143],"between":[144],"normal":[145],"malignant":[147,225],"tissue":[148],"liver,":[151],"while":[152,205],"method":[155,249],"computes":[156],"multi-class":[157],"found.":[162],"goal":[164],"research":[167],"come":[170],"up":[171],"an":[173,234],"unbiased":[174],"forecast":[175],"independent":[178],"human":[180],"error.":[181],"Our":[182,211],"proposed":[183,212],"method,":[184],"other":[187],"hand,":[188],"nearly":[189],"equals":[190],"top":[192],"performance":[196],"provides":[198],"highest":[200],"precision":[201],"lesion":[203],"identification":[204],"keeping":[206],"high":[208],"recall":[209],"value.":[210],"approach":[213,267],"correctly":[214],"classifies":[215],"identified":[216],"into":[219],"three":[220],"categories:":[221],"hepatocellular":[222],"carcinomas":[223],"(HCC),":[224],"(other":[226],"than":[227],"HCCs)":[228],"benign":[230],"or":[231],"cyst":[232],"average":[235],"accuracy":[236],"87.8%.":[238],"novelty":[240],"paper":[243],"lies":[244],"designing":[246],"mask-RCNN-based":[248],"portion,":[255],"implementing":[256],"segment":[259],"lesions":[261],"using":[263],"CNN-based":[266],"categorise":[269],"masses.":[271]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":18}],"updated_date":"2026-07-17T09:13:05.818461","created_date":"2025-10-10T00:00:00"}
