{"id":"https://openalex.org/W4312652333","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892036","title":"Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks","display_name":"Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312652333","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892036"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892036","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5017180156","display_name":"Guilherme Vieira","orcid":"https://orcid.org/0000-0003-3361-6154"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Guilherme Vieira","raw_affiliation_strings":["University of Campinas,Department of Applied Mathematics,Campinas,Brazil","Department of Applied Mathematics, University of Campinas, Campinas, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Campinas,Department of Applied Mathematics,Campinas,Brazil","institution_ids":["https://openalex.org/I181391015"]},{"raw_affiliation_string":"Department of Applied Mathematics, University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012009095","display_name":"Marcos Eduardo Valle","orcid":"https://orcid.org/0000-0003-4026-5110"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marcos Eduardo Valle","raw_affiliation_strings":["University of Campinas,Department of Applied Mathematics,Campinas,Brazil","Department of Applied Mathematics, University of Campinas, Campinas, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Campinas,Department of Applied Mathematics,Campinas,Brazil","institution_ids":["https://openalex.org/I181391015"]},{"raw_affiliation_string":"Department of Applied Mathematics, University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181391015"],"apc_list":null,"apc_paid":null,"fwci":1.8492,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.90862177,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9998999834060669,"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":0.9998999834060669,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9922999739646912,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hypercomplex-number","display_name":"Hypercomplex number","score":0.8936826586723328},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7921097278594971},{"id":"https://openalex.org/keywords/blood-cancer","display_name":"Blood cancer","score":0.5972298979759216},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5840712189674377},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5645411610603333},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5287353992462158},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4980142116546631},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4577658474445343},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4425567388534546},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.42779165506362915},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27297139167785645},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.16682222485542297},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1431555151939392}],"concepts":[{"id":"https://openalex.org/C203249530","wikidata":"https://www.wikidata.org/wiki/Q837414","display_name":"Hypercomplex number","level":3,"score":0.8936826586723328},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7921097278594971},{"id":"https://openalex.org/C2992972558","wikidata":"https://www.wikidata.org/wiki/Q2509220","display_name":"Blood cancer","level":3,"score":0.5972298979759216},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5840712189674377},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5645411610603333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5287353992462158},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4980142116546631},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4577658474445343},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4425567388534546},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.42779165506362915},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27297139167785645},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.16682222485542297},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1431555151939392},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C200127275","wikidata":"https://www.wikidata.org/wiki/Q173853","display_name":"Quaternion","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892036","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5899999737739563}],"awards":[{"id":"https://openalex.org/G3260351116","display_name":null,"funder_award_id":"315820/2021-7","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"},{"id":"https://openalex.org/G3522713804","display_name":null,"funder_award_id":"2019/02278-2","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"}],"funders":[{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W592889898","https://openalex.org/W1989757000","https://openalex.org/W1999492273","https://openalex.org/W2000897614","https://openalex.org/W2008233223","https://openalex.org/W2024467433","https://openalex.org/W2049014136","https://openalex.org/W2089824201","https://openalex.org/W2112796928","https://openalex.org/W2136704614","https://openalex.org/W2509399620","https://openalex.org/W2515973928","https://openalex.org/W2884745705","https://openalex.org/W2884747833","https://openalex.org/W2890197985","https://openalex.org/W2963230471","https://openalex.org/W2966878259","https://openalex.org/W2972638668","https://openalex.org/W3000552275","https://openalex.org/W3004102150","https://openalex.org/W3038805974","https://openalex.org/W3042758249","https://openalex.org/W3047932858","https://openalex.org/W3091222154","https://openalex.org/W3099478137","https://openalex.org/W3111295039","https://openalex.org/W3125691543","https://openalex.org/W3126374782","https://openalex.org/W3162323063","https://openalex.org/W3170484644","https://openalex.org/W3183597963","https://openalex.org/W3216179994","https://openalex.org/W4200105175","https://openalex.org/W4200553622","https://openalex.org/W4205127725","https://openalex.org/W4287592981","https://openalex.org/W6795273842"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951492339","https://openalex.org/W3167935049","https://openalex.org/W2964954556","https://openalex.org/W3029198973","https://openalex.org/W3019910406","https://openalex.org/W4281919838"],"abstract_inverted_index":{"This":[0],"paper":[1],"features":[2],"convolutional":[3,45,52],"neural":[4,46],"networks":[5,47],"defined":[6],"on":[7,81],"hypercomplex":[8],"algebras":[9,83],"applied":[10],"to":[11,115],"classify":[12],"lymphocytes":[13],"in":[14],"blood":[15,35],"smear":[16],"digital":[17],"microscopic":[18],"images.":[19],"Such":[20],"classification":[21,40],"is":[22],"helpful":[23],"for":[24],"the":[25,39,62,88,103,116],"diagnosis":[26],"of":[27,34,73,100],"acute":[28],"lymphoblast":[29],"leukemia":[30],"(ALL),":[31],"a":[32,69,107,111,121],"type":[33],"cancer.":[36],"We":[37],"perform":[38,59],"task":[41],"using":[42,102,120],"eight":[43],"hypercomplex-valued":[44],"(HvCNNs)":[48],"along":[49],"with":[50,68,106,125],"real-valued":[51,63],"networks.":[53],"Our":[54],"results":[55],"show":[56],"that":[57,78],"HvCNNs":[58,79],"better":[60],"than":[61],"model,":[64],"showcasing":[65],"higher":[66],"accuracy":[67,98],"much":[70,122],"smaller":[71],"number":[72],"parameters.":[74,128],"Moreover,":[75],"we":[76],"found":[77],"based":[80],"Clifford":[82],"processing":[84],"HSV-encoded":[85],"images":[86],"attained":[87],"highest":[89],"observed":[90],"accuracies.":[91],"Precisely,":[92],"our":[93],"HvCNN":[94],"yielded":[95],"an":[96],"average":[97],"rate":[99],"96.6%":[101],"ALL-IDB2":[104],"dataset":[105],"50%":[108],"train-test":[109],"split,":[110],"value":[112],"extremely":[113],"close":[114],"state-of-the-art":[117],"models":[118],"but":[119],"simpler":[123],"architecture":[124],"significantly":[126],"fewer":[127]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
