{"id":"https://openalex.org/W3210804650","doi":"https://doi.org/10.1080/08839514.2021.1995974","title":"Automatic Detection of Acute Lymphoblastic Leukemia Using UNET Based Segmentation and Statistical Analysis of Fused Deep Features","display_name":"Automatic Detection of Acute Lymphoblastic Leukemia Using UNET Based Segmentation and Statistical Analysis of Fused Deep Features","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3210804650","doi":"https://doi.org/10.1080/08839514.2021.1995974","mag":"3210804650"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2021.1995974","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.1995974","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.1995974?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"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":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.1995974?needAccess=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042161058","display_name":"S. Alagu","orcid":null},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"S Alagu","raw_affiliation_strings":["Department of Electronics Engineering, Mit Campus, Anna University, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Mit Campus, Anna University, Chennai, India","institution_ids":["https://openalex.org/I33585257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071799428","display_name":"Ahana Priyanka","orcid":"https://orcid.org/0000-0002-8487-1043"},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ahana Priyanka N","raw_affiliation_strings":["Department of Electronics Engineering, Mit Campus, Anna University, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Mit Campus, Anna University, Chennai, India","institution_ids":["https://openalex.org/I33585257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029673673","display_name":"G. Kavitha","orcid":"https://orcid.org/0000-0002-3041-0006"},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kavitha G","raw_affiliation_strings":["Department of Electronics Engineering, Mit Campus, Anna University, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Mit Campus, Anna University, Chennai, India","institution_ids":["https://openalex.org/I33585257"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085515440","display_name":"Bhoopathy Bagan K","orcid":null},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bhoopathy Bagan K","raw_affiliation_strings":["Department of Electronics Engineering, Mit Campus, Anna University, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Mit Campus, Anna University, Chennai, India","institution_ids":["https://openalex.org/I33585257"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042161058"],"corresponding_institution_ids":["https://openalex.org/I33585257"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":1.5517,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.85312868,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"35","issue":"15","first_page":"1952","last_page":"1969"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":1.0,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":1.0,"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":0.9751999974250793,"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/T10616","display_name":"Smart Agriculture and AI","score":0.9182000160217285,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7869274616241455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7341258525848389},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7254406809806824},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6963645815849304},{"id":"https://openalex.org/keywords/lymphoblastic-leukemia","display_name":"Lymphoblastic Leukemia","score":0.6608844995498657},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6591271162033081},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6079837083816528},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4735364615917206},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4222252070903778},{"id":"https://openalex.org/keywords/leukemia","display_name":"Leukemia","score":0.26784688234329224},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.08716908097267151}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7869274616241455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7341258525848389},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7254406809806824},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6963645815849304},{"id":"https://openalex.org/C2909962599","wikidata":"https://www.wikidata.org/wiki/Q180664","display_name":"Lymphoblastic Leukemia","level":3,"score":0.6608844995498657},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6591271162033081},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6079837083816528},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4735364615917206},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4222252070903778},{"id":"https://openalex.org/C2778461978","wikidata":"https://www.wikidata.org/wiki/Q29496","display_name":"Leukemia","level":2,"score":0.26784688234329224},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.08716908097267151},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2021.1995974","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.1995974","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.1995974?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"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":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1b07557a065c4e56b49c970b43a14d07","is_oa":false,"landing_page_url":"https://doaj.org/article/1b07557a065c4e56b49c970b43a14d07","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 35, Iss 15, Pp 1952-1969 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/08839514.2021.1995974","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.1995974","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.1995974?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"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":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6700000166893005,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3210804650.pdf","grobid_xml":"https://content.openalex.org/works/W3210804650.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W564766431","https://openalex.org/W2020619173","https://openalex.org/W2078658728","https://openalex.org/W2131987814","https://openalex.org/W2154053567","https://openalex.org/W2188383613","https://openalex.org/W2563823404","https://openalex.org/W2586483808","https://openalex.org/W2606429533","https://openalex.org/W2762113702","https://openalex.org/W2801148351","https://openalex.org/W2801624633","https://openalex.org/W2808349803","https://openalex.org/W2808546616","https://openalex.org/W2893154092","https://openalex.org/W2898491665","https://openalex.org/W2932722080","https://openalex.org/W2954203073","https://openalex.org/W2959123891","https://openalex.org/W2977638948","https://openalex.org/W2995027876","https://openalex.org/W2998825217","https://openalex.org/W3009226711","https://openalex.org/W3015925452","https://openalex.org/W3036366117","https://openalex.org/W3042228502","https://openalex.org/W3045628539","https://openalex.org/W3086545655","https://openalex.org/W3092530991","https://openalex.org/W3093187293","https://openalex.org/W3093305443","https://openalex.org/W3094854595","https://openalex.org/W3097581618","https://openalex.org/W3119005666","https://openalex.org/W3137244318","https://openalex.org/W3140854437","https://openalex.org/W3170241800","https://openalex.org/W4210404451","https://openalex.org/W4212962367","https://openalex.org/W4365799970"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Acute":[0],"lymphoblastic":[1],"leukemia":[2],"(ALL)":[3],"in":[4],"human":[5],"white":[6],"blood":[7],"cells":[8,59,194],"is":[9,23,60,119,137,149,176,180],"hazardous":[10],"and":[11,57,84,86,106,142,192,198],"requires":[12],"immediate":[13],"clinical":[14,200],"interventions.":[15],"The":[16,33,52,92,167],"main":[17],"objective":[18],"of":[19,31,54,74,116,153,169],"the":[20,26,43,70,114,124,134,154,170,190],"proposed":[21,171],"work":[22],"to":[24,122,159,188,195],"suggest":[25],"predominant":[27],"features":[28,66,88,94,118,157,184],"for":[29],"detection":[30],"ALL.":[32],"input":[34],"images":[35,45],"are":[36,46,67,89,95,129,185],"obtained":[37,44],"from":[38,69],"public":[39],"database":[40],"\u2018ALL-IDB2\u02b9.":[41],"All":[42],"resized":[47],"into":[48],"a":[49],"uniform":[50],"size.":[51],"nucleus":[53],"both":[55],"healthy":[56,191],"blast":[58,193],"segmented":[61],"using":[62,97,146],"UNET.":[63],"Thousand":[64],"deep":[65,126,156],"extracted":[68],"fully":[71],"connected":[72],"layer":[73],"different":[75],"convolutional":[76],"neural":[77],"network":[78],"models":[79],"such":[80],"as":[81],"AlexNet,":[82],"GoogleNet":[83],"SqueezeNet,":[85],"all":[87],"fused":[90,155,183],"together.":[91],"distinct":[93],"selected":[96,117],"mutual":[98],"information":[99],"(MI),":[100],"minimum":[101],"recursive":[102,107],"maximal":[103],"relevance":[104],"(mRmR)":[105],"feature":[108,144,174],"elimination":[109],"(RFE)":[110],"based":[111],"methods.":[112],"Furthermore,":[113],"intersection":[115],"carried":[120,138],"out":[121,139],"obtain":[123],"prominent":[125],"features,":[127],"which":[128],"examined":[130],"by":[131],"heatmap.":[132],"Finally,":[133],"statistical":[135],"analysis":[136],"with":[140,162],"consistent":[141],"robust":[143],"sets":[145],"ANOVA.":[147],"It":[148,179],"found":[150],"that":[151,182],"50%":[152],"seem":[158],"be":[160],"better":[161],"p":[163],"=":[164],".":[165],"00001.":[166],"performance":[168],"system":[172],"without":[173],"fusion":[175],"also":[177],"observed.":[178],"detected":[181],"more":[186],"suitable":[187],"discriminate":[189],"identify":[196],"ALL":[197],"support":[199],"decisions.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":7}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
