{"id":"https://openalex.org/W2532538358","doi":"https://doi.org/10.1109/istt.2014.7238192","title":"Hep-2 cell images fluorescence intensity classification to determine positivity based on neural network","display_name":"Hep-2 cell images fluorescence intensity classification to determine positivity based on neural network","publication_year":2014,"publication_date":"2014-11-01","ids":{"openalex":"https://openalex.org/W2532538358","doi":"https://doi.org/10.1109/istt.2014.7238192","mag":"2532538358"},"language":"en","primary_location":{"id":"doi:10.1109/istt.2014.7238192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/istt.2014.7238192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT)","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/A5034565290","display_name":"M. Zazilah","orcid":null},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"M. Zazilah","raw_affiliation_strings":["Electrical & Electronics Department, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia"],"affiliations":[{"raw_affiliation_string":"Electrical & Electronics Department, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005568938","display_name":"Ahmad Fairuzabadi Mohd Mansor","orcid":"https://orcid.org/0000-0002-2639-1703"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"A. F. Mansor","raw_affiliation_strings":["Electrical & Electronics Department, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia"],"affiliations":[{"raw_affiliation_string":"Electrical & Electronics Department, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073207630","display_name":"Nor Zaihar Yahaya","orcid":"https://orcid.org/0000-0002-6167-0863"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"N. Z. Yahaya","raw_affiliation_strings":["Electrical & Electronics Department, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia"],"affiliations":[{"raw_affiliation_string":"Electrical & Electronics Department, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034565290"],"corresponding_institution_ids":["https://openalex.org/I203899302"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.34699598,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":null,"first_page":"138","last_page":"143"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10308","display_name":"Systemic Lupus Erythematosus Research","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2745","display_name":"Rheumatology"},"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/T10308","display_name":"Systemic Lupus Erythematosus Research","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2745","display_name":"Rheumatology"},"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/T12867","display_name":"Advanced Biosensing Techniques and Applications","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"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"}},{"id":"https://openalex.org/T12955","display_name":"Atherosclerosis and Cardiovascular Diseases","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/2403","display_name":"Immunology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7378292679786682},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6907023191452026},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6869663000106812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6255574226379395},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5733380317687988},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5500206351280212},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5451556444168091},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5300868153572083},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.44146299362182617},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4223375916481018}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7378292679786682},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6907023191452026},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6869663000106812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6255574226379395},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5733380317687988},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5500206351280212},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5451556444168091},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5300868153572083},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.44146299362182617},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4223375916481018}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/istt.2014.7238192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/istt.2014.7238192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1583884748","https://openalex.org/W1968803632","https://openalex.org/W1976200264","https://openalex.org/W1996765463","https://openalex.org/W2002016471","https://openalex.org/W2028072395","https://openalex.org/W2032201831","https://openalex.org/W2071061339","https://openalex.org/W2090216846","https://openalex.org/W2099620731","https://openalex.org/W2122092925","https://openalex.org/W2123904821","https://openalex.org/W2125168731","https://openalex.org/W2134092095","https://openalex.org/W2138798535","https://openalex.org/W2138927287","https://openalex.org/W2146862352","https://openalex.org/W2163714625","https://openalex.org/W2164925651","https://openalex.org/W2183915566","https://openalex.org/W3102042549","https://openalex.org/W6642302830","https://openalex.org/W6686513095"],"related_works":["https://openalex.org/W2397288865","https://openalex.org/W2368524271","https://openalex.org/W2576709312","https://openalex.org/W2079402751","https://openalex.org/W2392797073","https://openalex.org/W2989490741","https://openalex.org/W2023657818","https://openalex.org/W2384907669","https://openalex.org/W2095030957","https://openalex.org/W2066827917"],"abstract_inverted_index":{"This":[0,89],"paper":[1],"applies":[2],"the":[3,28,48,51,66,77,120,131],"concept":[4],"of":[5,14,43,53,68,122,134,169],"Artificial":[6],"Neural":[7],"Network":[8],"(ANN)":[9],"to":[10,83,103,119,128],"classify":[11],"fluorescence":[12,45],"intensity":[13,46],"Hep-2":[15],"cell":[16,144],"images":[17],"into":[18],"three":[19],"classes;":[20],"positive,":[21],"intermediate":[22,132],"and":[23,65,85,113],"negative":[24],"auto-immune":[25],"disease.":[26],"Recently,":[27],"recommended":[29],"method":[30],"for":[31,62,146],"detection":[32],"antinuclear":[33],"auto-antibodies":[34],"(ANA)":[35],"is":[36,126,160,164],"Indirect":[37],"Immunofluorescence":[38],"(IIF).":[39],"The":[40,137,150],"diagnosis":[41],"consists":[42],"estimating":[44],"in":[47],"cells.":[49],"Since":[50],"increasing":[52],"test":[54],"demands,":[55],"trained":[56],"personnel":[57],"are":[58],"not":[59],"always":[60],"available":[61],"these":[63],"tasks":[64],"identification":[67],"positivity":[69],"has":[70,139],"recently":[71],"done":[72],"manually":[73],"by":[74],"human":[75],"analyzing":[76],"slide":[78],"with":[79],"a":[80,101,154],"microscope,":[81],"leading":[82],"subjective":[84],"bad":[86],"quality":[87],"results.":[88],"work":[90],"will":[91],"develop":[92],"Computer":[93],"Aided":[94],"Diagnosis":[95],"(CAD)":[96],"tools":[97],"that":[98,125],"can":[99],"offer":[100],"support":[102],"physician":[104],"decision.":[105],"Then,":[106],"it":[107],"discusses":[108],"image":[109,111],"preprocessing,":[110],"segmentation":[112],"feature":[114],"extraction.":[115],"Later,":[116],"this":[117,163],"lead":[118],"proposal":[121],"ANN-based":[123],"classifier":[124],"able":[127],"separate":[129],"essentially":[130],"sample":[133],"ANA":[135],"diseases.":[136],"approach":[138],"been":[140],"evaluated":[141],"using":[142],"142":[143],"images,":[145],"372":[147],"training":[148],"data.":[149],"measured":[151],"performance":[152],"shows":[153],"low":[155],"overall":[156],"error":[157,167],"rate":[158,168],"which":[159],"3":[161],"%,":[162],"lower":[165],"than":[166],"observed":[170],"intra-laboratory":[171],"variability.":[172]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
