{"id":"https://openalex.org/W4388893577","doi":"https://doi.org/10.1109/tencon58879.2023.10322346","title":"Breast Density Classification to Aid Clinical Workflow in Breast Cancer Detection Using Deep Learning Network","display_name":"Breast Density Classification to Aid Clinical Workflow in Breast Cancer Detection Using Deep Learning Network","publication_year":2023,"publication_date":"2023-10-31","ids":{"openalex":"https://openalex.org/W4388893577","doi":"https://doi.org/10.1109/tencon58879.2023.10322346"},"language":"en","primary_location":{"id":"doi:10.1109/tencon58879.2023.10322346","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon58879.2023.10322346","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)","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/A5029929756","display_name":"K B Saran","orcid":null},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K B Saran","raw_affiliation_strings":["National Institute of Technology,Calicut","National Institute of Technology, Calicut"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology,Calicut","institution_ids":["https://openalex.org/I114845381"]},{"raw_affiliation_string":"National Institute of Technology, Calicut","institution_ids":["https://openalex.org/I114845381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015942038","display_name":"G. Sreelekha","orcid":null},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"G Sreelekha","raw_affiliation_strings":["National Institute of Technology,Calicut","National Institute of Technology, Calicut"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology,Calicut","institution_ids":["https://openalex.org/I114845381"]},{"raw_affiliation_string":"National Institute of Technology, Calicut","institution_ids":["https://openalex.org/I114845381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043201657","display_name":"V C Sunitha","orcid":null},"institutions":[{"id":"https://openalex.org/I61923386","display_name":"Jawaharlal Institute of Post Graduate Medical Education and Research","ror":"https://ror.org/02fq2px14","country_code":"IN","type":"education","lineage":["https://openalex.org/I61923386"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"V C Sunitha","raw_affiliation_strings":["Jawaharlal Institute of Postgraduate Medical Education and Research,Pondicherry","Jawaharlal Institute of Postgraduate Medical Education and Research, Pondicherry"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jawaharlal Institute of Postgraduate Medical Education and Research,Pondicherry","institution_ids":["https://openalex.org/I61923386"]},{"raw_affiliation_string":"Jawaharlal Institute of Postgraduate Medical Education and Research, Pondicherry","institution_ids":["https://openalex.org/I61923386"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7126,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.75780541,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"444","last_page":"449"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9994000196456909,"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.9850999712944031,"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/breast-density","display_name":"Breast density","score":0.7628252506256104},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.7571245431900024},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6574221849441528},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5957939028739929},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5651349425315857},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5572959780693054},{"id":"https://openalex.org/keywords/digital-breast-tomosynthesis","display_name":"Digital Breast Tomosynthesis","score":0.5570095777511597},{"id":"https://openalex.org/keywords/breast-tissue","display_name":"Breast tissue","score":0.5040875673294067},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.501824140548706},{"id":"https://openalex.org/keywords/breast-imaging","display_name":"Breast imaging","score":0.5007965564727783},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4681394696235657},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4357498288154602},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4350908100605011},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.4273563623428345},{"id":"https://openalex.org/keywords/breast-cancer-screening","display_name":"Breast cancer screening","score":0.42675572633743286},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3563344478607178},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.2068408727645874},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.20438948273658752},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13457918167114258},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.12439817190170288},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.11831870675086975}],"concepts":[{"id":"https://openalex.org/C3018951153","wikidata":"https://www.wikidata.org/wiki/Q17011492","display_name":"Breast density","level":5,"score":0.7628252506256104},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.7571245431900024},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6574221849441528},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5957939028739929},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5651349425315857},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5572959780693054},{"id":"https://openalex.org/C2909182381","wikidata":"https://www.wikidata.org/wiki/Q7820316","display_name":"Digital Breast Tomosynthesis","level":5,"score":0.5570095777511597},{"id":"https://openalex.org/C3020109028","wikidata":"https://www.wikidata.org/wiki/Q9103","display_name":"Breast tissue","level":4,"score":0.5040875673294067},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.501824140548706},{"id":"https://openalex.org/C2777432617","wikidata":"https://www.wikidata.org/wiki/Q22905905","display_name":"Breast imaging","level":5,"score":0.5007965564727783},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4681394696235657},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4357498288154602},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4350908100605011},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.4273563623428345},{"id":"https://openalex.org/C2778491387","wikidata":"https://www.wikidata.org/wiki/Q17011492","display_name":"Breast cancer screening","level":5,"score":0.42675572633743286},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3563344478607178},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.2068408727645874},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.20438948273658752},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13457918167114258},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.12439817190170288},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.11831870675086975}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon58879.2023.10322346","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon58879.2023.10322346","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)","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":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W304373761","https://openalex.org/W2001412060","https://openalex.org/W2064548719","https://openalex.org/W2137839901","https://openalex.org/W2194775991","https://openalex.org/W2586329273","https://openalex.org/W2771007923","https://openalex.org/W2890514615","https://openalex.org/W2897490102","https://openalex.org/W3007228139","https://openalex.org/W3033895395","https://openalex.org/W3097182350","https://openalex.org/W3131637220","https://openalex.org/W3163936961","https://openalex.org/W4206133707","https://openalex.org/W4225283717","https://openalex.org/W4236965008","https://openalex.org/W4280561280","https://openalex.org/W6725757660","https://openalex.org/W6795875376"],"related_works":["https://openalex.org/W2138691429","https://openalex.org/W2154027915","https://openalex.org/W2344745381","https://openalex.org/W2121126942","https://openalex.org/W183642269","https://openalex.org/W2334042002","https://openalex.org/W1985438294","https://openalex.org/W2888661800","https://openalex.org/W2278772159","https://openalex.org/W4251953844"],"abstract_inverted_index":{"Breast":[0,87],"density":[1,71,95,113],"is":[2,33,105],"an":[3,145],"important":[4],"bio-marker":[5],"for":[6,66,107],"predicting":[7],"the":[8,45,51,62,108,117,137,141],"risk":[9],"of":[10,24,36,38,47,78,111],"breast":[11,20,25,43,58,67,80,94,112],"dancer.":[12],"Studies":[13],"have":[14,21],"showed":[15],"that":[16],"women":[17],"with":[18],"dense":[19,79],"higher":[22],"probability":[23],"cancer.":[26],"The":[27,120,133],"density,":[28],"assessed":[29],"using":[30,81,97],"mammogram":[31],"images,":[32],"a":[34,42,70,93],"measure":[35],"amount":[37],"fibro-glandular":[39],"tissues":[40],"in":[41,57,116,140],"and":[44,131,149,158],"appearance":[46],"it":[48],"can":[49],"mask":[50],"lesions":[52],"leading":[53],"to":[54,114],"lesser":[55],"sensitivity":[56],"cancer":[59],"detection.":[60],"Hence":[61],"current":[63],"clinical":[64,118],"workflow":[65],"detection":[68],"incorporates":[69],"classification":[72,110],"stage":[73],"which":[74],"suggest":[75],"detailed":[76],"analysis":[77],"additional":[82],"imaging":[83],"modalities":[84],"like":[85],"Digital":[86],"Tomosynthesis":[88],"(DBT).":[89],"In":[90],"this":[91],"work":[92],"classifier":[96],"deep":[98],"neural":[99],"network":[100],"architecture":[101],"employing":[102],"transfer":[103],"learning":[104],"proposed":[106,134],"binary":[109],"assist":[115],"workflows.":[119],"results":[121],"are":[122],"evaluated":[123],"on":[124,144,156],"publicly":[125],"available":[126],"databases":[127],"namely":[128],"DDSM,":[129],"MIAS":[130,159],"InBreast.":[132],"model":[135],"outperforms":[136],"existing":[138],"works":[139],"literature":[142],"giving":[143],"average":[146],"96":[147],"%":[148,151],"94":[150],"accuracy":[152],"respectively,":[153],"when":[154],"tested":[155],"DDSM":[157],"database.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}