{"id":"https://openalex.org/W2790059626","doi":"https://doi.org/10.1117/12.2293498","title":"End-to-end breast ultrasound lesions recognition with a deep learning approach","display_name":"End-to-end breast ultrasound lesions recognition with a deep learning approach","publication_year":2018,"publication_date":"2018-03-12","ids":{"openalex":"https://openalex.org/W2790059626","doi":"https://doi.org/10.1117/12.2293498","mag":"2790059626"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293498","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging","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/A5089717792","display_name":"Fatima Osman","orcid":null},"institutions":[{"id":"https://openalex.org/I9353105","display_name":"Sudan University of Science and Technology","ror":"https://ror.org/02fwtg066","country_code":"SD","type":"education","lineage":["https://openalex.org/I9353105"]}],"countries":["SD"],"is_corresponding":true,"raw_author_name":"Fatima M. Osman","raw_affiliation_strings":["Sudan Univ. of Science and Technology (Sudan)"],"affiliations":[{"raw_affiliation_string":"Sudan Univ. of Science and Technology (Sudan)","institution_ids":["https://openalex.org/I9353105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074533738","display_name":"Robert Mart\u00ed","orcid":"https://orcid.org/0000-0002-8080-2710"},"institutions":[{"id":"https://openalex.org/I251424209","display_name":"University of Girona","ror":"https://ror.org/01xdxns91","country_code":"ES","type":"education","lineage":["https://openalex.org/I251424209"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Robert Marti","raw_affiliation_strings":["Univ. de Girona (Spain)"],"affiliations":[{"raw_affiliation_string":"Univ. de Girona (Spain)","institution_ids":["https://openalex.org/I251424209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075394907","display_name":"Reyer Zwiggelaar","orcid":"https://orcid.org/0000-0002-4360-0896"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reyer Zwiggelaar","raw_affiliation_strings":["Aberystwyth Univ. (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Aberystwyth Univ. (United Kingdom)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051038711","display_name":"Arne Juette","orcid":"https://orcid.org/0009-0008-2825-7875"},"institutions":[{"id":"https://openalex.org/I2800371493","display_name":"Norfolk and Norwich University Hospitals NHS Foundation Trust","ror":"https://ror.org/01wspv808","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I2800371493"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Arne Juette","raw_affiliation_strings":["Norfolk and Norwich Univ. Hospital Foundation Trust (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Norfolk and Norwich Univ. Hospital Foundation Trust (United Kingdom)","institution_ids":["https://openalex.org/I2800371493"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068488957","display_name":"Erika Denton","orcid":"https://orcid.org/0000-0001-8650-862X"},"institutions":[{"id":"https://openalex.org/I2800371493","display_name":"Norfolk and Norwich University Hospitals NHS Foundation Trust","ror":"https://ror.org/01wspv808","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I2800371493"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Erika Denton","raw_affiliation_strings":["Norfolk and Norwich Univ. Hospital Foundation Trust (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Norfolk and Norwich Univ. Hospital Foundation Trust (United Kingdom)","institution_ids":["https://openalex.org/I2800371493"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037771946","display_name":"Moi Hoon Yap","orcid":"https://orcid.org/0000-0001-7681-4287"},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Moi Hoon Yap","raw_affiliation_strings":["Manchester Metropolitan Univ. (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Manchester Metropolitan Univ. (United Kingdom)","institution_ids":["https://openalex.org/I11983389"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013477732","display_name":"Manu Goyal","orcid":"https://orcid.org/0000-0002-9201-1385"},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Manu Goyal","raw_affiliation_strings":["Manchester Metropolitan Univ. (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Manchester Metropolitan Univ. (United Kingdom)","institution_ids":["https://openalex.org/I11983389"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078545447","display_name":"Ezak Fadzrin Ahmad Shaubari","orcid":"https://orcid.org/0000-0001-9741-6819"},"institutions":[{"id":"https://openalex.org/I16038530","display_name":"Aberystwyth University","ror":"https://ror.org/015m2p889","country_code":"GB","type":"education","lineage":["https://openalex.org/I16038530"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ezak Fadzrin B. Ahmad-Shaubari","raw_affiliation_strings":["Aberystwyth University, United Kingdom >  >  >  >"],"affiliations":[{"raw_affiliation_string":"Aberystwyth University, United Kingdom >  >  >  >","institution_ids":["https://openalex.org/I16038530"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5089717792"],"corresponding_institution_ids":["https://openalex.org/I9353105"],"apc_list":null,"apc_paid":null,"fwci":3.4206,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.93880724,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"7259","issue":null,"first_page":"44","last_page":"44"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9969000220298767,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9969000220298767,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9922000169754028,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9681000113487244,"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":0.6326195597648621},{"id":"https://openalex.org/keywords/breast-ultrasound","display_name":"Breast ultrasound","score":0.5370537042617798},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.5320476293563843},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5077787041664124},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4924846589565277},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.4859570264816284},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3862749934196472},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.2746826410293579},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.22857597470283508},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.19420850276947021},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.13754603266716003},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.09797561168670654}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6326195597648621},{"id":"https://openalex.org/C2777423100","wikidata":"https://www.wikidata.org/wiki/Q1888238","display_name":"Breast ultrasound","level":5,"score":0.5370537042617798},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.5320476293563843},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5077787041664124},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4924846589565277},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.4859570264816284},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3862749934196472},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2746826410293579},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.22857597470283508},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.19420850276947021},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.13754603266716003},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.09797561168670654},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2293498","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W46659105","https://openalex.org/W1903029394","https://openalex.org/W1974859069","https://openalex.org/W1988819287","https://openalex.org/W2003188860","https://openalex.org/W2004483938","https://openalex.org/W2011585383","https://openalex.org/W2037227137","https://openalex.org/W2047913312","https://openalex.org/W2086268663","https://openalex.org/W2094872149","https://openalex.org/W2101521775","https://openalex.org/W2110505741","https://openalex.org/W2125916419","https://openalex.org/W2146502635","https://openalex.org/W2153062028","https://openalex.org/W2163287977","https://openalex.org/W2163605009","https://openalex.org/W2307162709","https://openalex.org/W2310992461","https://openalex.org/W2472971850","https://openalex.org/W2529926598","https://openalex.org/W2609077090","https://openalex.org/W2744692634","https://openalex.org/W2950094539","https://openalex.org/W2964263369","https://openalex.org/W4253696451","https://openalex.org/W4298274253","https://openalex.org/W4298442188","https://openalex.org/W4300812599","https://openalex.org/W4301329587","https://openalex.org/W6602002561","https://openalex.org/W6640054144","https://openalex.org/W6651364768","https://openalex.org/W6678869746","https://openalex.org/W6681435938","https://openalex.org/W6684191040","https://openalex.org/W6694212129","https://openalex.org/W6697996855","https://openalex.org/W6728528290","https://openalex.org/W6737170303","https://openalex.org/W6742646320","https://openalex.org/W6745493755","https://openalex.org/W6746095478","https://openalex.org/W6764076272"],"related_works":["https://openalex.org/W3108039862","https://openalex.org/W2618954822","https://openalex.org/W1990605602","https://openalex.org/W31474833","https://openalex.org/W2348815573","https://openalex.org/W4283075545","https://openalex.org/W2070388411","https://openalex.org/W2003113500","https://openalex.org/W2896805445","https://openalex.org/W2101706338"],"abstract_inverted_index":{"Existing":[0],"methods":[1],"for":[2,71,120,124,202,210],"automated":[3],"breast":[4,48,203],"ultrasound":[5,49,204],"lesions":[6,50,154,176,188],"detection":[7,51],"and":[8,22,52,86,104,127,180,192,219],"recognition":[9,53],"tend":[10],"to":[11,151,215,225],"be":[12],"based":[13,83],"on":[14,31,84,93,140],"multi-stage":[15],"processing,":[16],"such":[17],"as":[18],"preprocessing,":[19],"filtering/denoising,":[20],"segmentation":[21,64],"classification.":[23],"The":[24,131,207],"performance":[25,111],"of":[26,40,98,101,112,147,159,165,173,185,229],"these":[27],"processes":[28],"is":[29],"dependent":[30],"the":[32,37,41,110,113,116,152,163,174,186,198,211,221,227],"prior":[33],"stages.":[34],"To":[35,74],"improve":[36,226],"current":[38],"state":[39],"art,":[42],"we":[43,78],"have":[44],"proposed":[45,136,212],"an":[46],"end-to-end":[47,200],"using":[54,115],"a":[55,61,80,99,144,156],"deep":[56,222],"learning":[57,223],"approach.":[58],"We":[59,89,108],"implemented":[60],"popular":[62],"semantic":[63],"framework,":[65],"i.e.":[66],"Fully":[67],"Convolutional":[68],"Network":[69],"(FCN-AlexNet)":[70],"our":[72,91,135],"experiment.":[73],"overcome":[75],"data":[76],"deficiency,":[77],"used":[79],"pre-trained":[81],"model":[82,114],"ImageNet":[85],"transfer":[87],"learning.":[88],"validated":[90],"results":[92,132],"two":[94],"datasets,":[95],"which":[96],"consist":[97],"total":[100],"113":[102],"malignant":[103,153,187],"356":[105],"benign":[106,141,175],"lesions.":[107],"assessed":[109],"following":[117],"split:":[118],"70&percnt;":[119],"training":[121],"data,":[122,126],"10&percnt;":[123],"validation":[125],"20&percnt;":[128],"testing":[129],"data.":[130],"show":[133],"that":[134],"method":[137],"performed":[138],"better":[139],"lesions,":[142],"with":[143,155,167],"<i>Dice</i>":[145,157,168],"score":[146,158,169],"0.6879,":[148],"when":[149],"compared":[150],"0.5525.":[160],"When":[161],"considering":[162],"number":[164],"images":[166],"&gt;":[170],"0.5,":[171],"79&percnt;":[172],"were":[177,189],"successfully":[178,190],"segmented":[179,191],"correctly":[181,193],"recognised,":[182],"while":[183],"65&percnt;":[184],"recognised.":[194],"This":[195],"paper":[196],"provides":[197],"first":[199],"solution":[201],"lesion":[205],"recognition.":[206],"future":[208],"challenges":[209],"approaches":[213],"are":[214],"obtain":[216],"additional":[217],"datasets":[218],"customize":[220],"framework":[224],"accuracy":[228],"this":[230],"method.":[231]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
