{"id":"https://openalex.org/W4200048114","doi":"https://doi.org/10.1109/iscc53001.2021.9631499","title":"Lightweight Classification of Normal Versus Leukemic Cells Using Feature Extraction","display_name":"Lightweight Classification of Normal Versus Leukemic Cells Using Feature Extraction","publication_year":2021,"publication_date":"2021-09-05","ids":{"openalex":"https://openalex.org/W4200048114","doi":"https://doi.org/10.1109/iscc53001.2021.9631499"},"language":"en","primary_location":{"id":"doi:10.1109/iscc53001.2021.9631499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc53001.2021.9631499","pdf_url":null,"source":{"id":"https://openalex.org/S4363605778","display_name":"2021 IEEE Symposium on Computers and Communications (ISCC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-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/A5113975561","display_name":"Yuri Faro Dantas De Sant' Anna","orcid":null},"institutions":[{"id":"https://openalex.org/I190085865","display_name":"Universidade Federal de Sergipe","ror":"https://ror.org/028ka0n85","country_code":"BR","type":"education","lineage":["https://openalex.org/I190085865"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Yuri Faro Dantas De Sant' Anna","raw_affiliation_strings":["Universidade Federal de Sergipe, S\u00e3o Crist\u00f3v\u00e3o, SE, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-3527-6862","affiliations":[{"raw_affiliation_string":"Universidade Federal de Sergipe, S\u00e3o Crist\u00f3v\u00e3o, SE, Brazil","institution_ids":["https://openalex.org/I190085865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014539990","display_name":"Jos\u00e9 Elwyslan Maur\u00edcio de Oliveira","orcid":"https://orcid.org/0000-0002-0282-0921"},"institutions":[{"id":"https://openalex.org/I190085865","display_name":"Universidade Federal de Sergipe","ror":"https://ror.org/028ka0n85","country_code":"BR","type":"education","lineage":["https://openalex.org/I190085865"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jose Elwyslan Mauricio De Oliveira","raw_affiliation_strings":["Universidade Federal de Sergipe, S\u00e3o Crist\u00f3v\u00e3o, SE, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-0282-0921","affiliations":[{"raw_affiliation_string":"Universidade Federal de Sergipe, S\u00e3o Crist\u00f3v\u00e3o, SE, Brazil","institution_ids":["https://openalex.org/I190085865"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040051282","display_name":"Daniel Oliveira Dantas","orcid":"https://orcid.org/0000-0002-0142-891X"},"institutions":[{"id":"https://openalex.org/I190085865","display_name":"Universidade Federal de Sergipe","ror":"https://ror.org/028ka0n85","country_code":"BR","type":"education","lineage":["https://openalex.org/I190085865"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Daniel Oliveira Dantas","raw_affiliation_strings":["Universidade Federal de Sergipe, S\u00e3o Crist\u00f3v\u00e3o, SE, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-0142-891X","affiliations":[{"raw_affiliation_string":"Universidade Federal de Sergipe, S\u00e3o Crist\u00f3v\u00e3o, SE, Brazil","institution_ids":["https://openalex.org/I190085865"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113975561"],"corresponding_institution_ids":["https://openalex.org/I190085865"],"apc_list":null,"apc_paid":null,"fwci":0.1313,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.48624642,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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":0.9807999730110168,"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":0.9793000221252441,"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/computer-science","display_name":"Computer science","score":0.7771898508071899},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7622493505477905},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7407318949699402},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6994488835334778},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6647452116012573},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6614214181900024},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6458753943443298},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.617490291595459},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5741998553276062},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3535105884075165},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.31557899713516235}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771898508071899},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7622493505477905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7407318949699402},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6994488835334778},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6647452116012573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6614214181900024},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6458753943443298},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.617490291595459},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5741998553276062},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3535105884075165},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31557899713516235}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscc53001.2021.9631499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc53001.2021.9631499","pdf_url":null,"source":{"id":"https://openalex.org/S4363605778","display_name":"2021 IEEE Symposium on Computers and Communications (ISCC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1545533962","https://openalex.org/W1994241566","https://openalex.org/W2000897614","https://openalex.org/W2035020726","https://openalex.org/W2066974172","https://openalex.org/W2088553141","https://openalex.org/W2484322924","https://openalex.org/W2557065683","https://openalex.org/W2567103521","https://openalex.org/W2567699853","https://openalex.org/W2593687058","https://openalex.org/W2606483217","https://openalex.org/W2753825789","https://openalex.org/W2767128594","https://openalex.org/W2798219973","https://openalex.org/W2860863020","https://openalex.org/W2888685797","https://openalex.org/W2890671308","https://openalex.org/W2893154092","https://openalex.org/W2898491665","https://openalex.org/W2922975151","https://openalex.org/W2950524061","https://openalex.org/W2990272592","https://openalex.org/W2990386861","https://openalex.org/W2990527191","https://openalex.org/W2990786037","https://openalex.org/W2990863204","https://openalex.org/W2990932989","https://openalex.org/W2991290986","https://openalex.org/W2991338315","https://openalex.org/W2991338768","https://openalex.org/W3131307993","https://openalex.org/W4232935862","https://openalex.org/W4237140380","https://openalex.org/W4239875977","https://openalex.org/W4289376883","https://openalex.org/W6731518377","https://openalex.org/W6770257149"],"related_works":["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","https://openalex.org/W2952813363","https://openalex.org/W2911497689","https://openalex.org/W4360783045"],"abstract_inverted_index":{"Current":[0],"deep":[1,55,113],"learning":[2,56,114,118],"approaches,":[3],"based":[4],"on":[5,68],"convolutional":[6],"neural":[7,94,121,140],"networks,":[8],"produce":[9],"models":[10],"with":[11,76,144],"excellent":[12],"performance":[13],"in":[14,116],"lymphocyte":[15,87],"classification":[16,38,129],"tasks.":[17],"However,":[18],"they":[19],"require":[20,59],"long":[21],"training":[22,62],"times":[23],"and":[24,45,54,64,81,89,120],"specific":[25],"hardware":[26],"to":[27,91],"work.":[28],"To":[29],"overcome":[30],"these":[31],"drawbacks,":[32],"we":[33],"present":[34],"a":[35,69,93,139,145],"lightweight":[36],"image":[37,52],"system":[39],"capable":[40],"of":[41,48,148],"discriminating":[42],"between":[43],"cancerous":[44],"noncancerous":[46],"lymphocytes":[47,99],"leukemia":[49,128],"patients":[50],"using":[51],"processing":[53],"techniques":[57],"that":[58,96,132],"much":[60],"less":[61],"time":[63,119],"can":[65,135],"be":[66,136],"run":[67],"standard":[70],"CPU.":[71],"Feature":[72],"vectors":[73],"are":[74],"built":[75],"statistical,":[77],"textural,":[78],"morphological,":[79],"contour,":[80],"frequency":[82],"features":[83],"extracted":[84],"from":[85],"each":[86],"image,":[88],"used":[90],"train":[92],"network":[95,122,141],"classifies":[97],"the":[98],"into":[100],"malignant":[101],"or":[102],"healthy.":[103],"The":[104],"proposed":[105],"method":[106],"has":[107],"lower":[108],"computational":[109],"cost":[110],"than":[111],"other":[112],"methods":[115],"both":[117],"size.":[123],"Our":[124],"results":[125],"contribute":[126],"towards":[127],"systems":[130],"showing":[131],"high":[133],"accuracy":[134],"achieved":[137],"by":[138],"classifier":[142],"trained":[143],"rich":[146],"set":[147],"features.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}