{"id":"https://openalex.org/W2765239258","doi":null,"title":"FISSURE DETECTION IN LUNG CT IMAGES USINGBACK PROPAGATION NEURAL NETWORKS","display_name":"FISSURE DETECTION IN LUNG CT IMAGES USINGBACK PROPAGATION NEURAL NETWORKS","publication_year":2017,"publication_date":"2017-10-16","ids":{"openalex":"https://openalex.org/W2765239258","mag":"2765239258"},"language":"en","primary_location":{"id":"mag:2765239258","doi":null,"is_oa":false,"landing_page_url":"https://www.icontrolpollution.com/articles/fissure-detection-in-lung-ct-images-usingback-propagation-neural-networks-.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S2764923910","display_name":"Journal of Industrial Pollution Control","issn_l":"0970-2083","issn":["0970-2083"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Industrial Pollution Control","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5062671157","display_name":"K. Devaki","orcid":"https://orcid.org/0000-0002-4805-8039","observed_orcids":["https://orcid.org/0000-0002-4805-8039"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K. Devaki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079278501","display_name":"Akila Devi R","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akila Devi R","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5064970675","display_name":"P Vidhupriya","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P Vidhupriya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":"2","first_page":"1571","last_page":"1578"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9643999934196472,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9643999934196472,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.013899999670684338,"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"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.007499999832361937,"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/lung-ct-images","display_name":"lung CT images","score":0.8190000057220459},{"id":"https://openalex.org/keywords/back-propagation-neural-network","display_name":"BP neural network","score":0.9169999957084656},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.8550000190734863},{"id":"https://openalex.org/keywords/supervised-classification","display_name":"supervised classification","score":0.7480000257492065},{"id":"https://openalex.org/keywords/pulmonary-diagnosis","display_name":"pulmonary diagnosis","score":0.3100000023841858}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7089441418647766},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6709523797035217},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6570600271224976},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.655434250831604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6311410665512085},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5891361236572266},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.4935554265975952},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.45962440967559814},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4369840621948242},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41669419407844543},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.41267621517181396},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2765239258","doi":null,"is_oa":false,"landing_page_url":"https://www.icontrolpollution.com/articles/fissure-detection-in-lung-ct-images-usingback-propagation-neural-networks-.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S2764923910","display_name":"Journal of Industrial Pollution Control","issn_l":"0970-2083","issn":["0970-2083"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Industrial Pollution Control","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8048999905586243}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2887477052","https://openalex.org/W2234035984","https://openalex.org/W7120833443","https://openalex.org/W2248496390","https://openalex.org/W7043317078","https://openalex.org/W4403302539","https://openalex.org/W2348409451","https://openalex.org/W289824575","https://openalex.org/W2025026714","https://openalex.org/W2106127561"],"abstract_inverted_index":{"Fissure":[0],"segmentation":[1],"and":[2,19,121],"detection":[3],"of":[4,21,99,132,156],"fissures":[5,49],"in":[6,12,24,103,108],"lung":[7],"CT":[8,147,151],"images":[9,148],"is":[10,38,63,71,88,160],"essential":[11],"the":[13,17,25,42,48,54,67,74,79,86,97,105,109,119,123,129,134,139,157],"clinical":[14],"practice":[15],"for":[16,85],"interpretation":[18],"diagnosis":[20],"diseases":[22],"present":[23,46],"lungs.":[26],"A":[27],"novel":[28],"supervised":[29],"learning":[30],"approach":[31],"using":[32],"multilayer":[33],"back":[34,114],"propagation":[35,115],"neural":[36,116],"networks":[37],"employed":[39],"to":[40,65,77,127,137,162],"segment":[41],"fissures.":[43],"The":[44,69,82,94,113,141,153],"pixels":[45],"on":[47,53],"are":[50],"classified":[51],"based":[52],"feature":[55,60],"set":[56],"calculation.":[57],"An":[58],"optimized":[59],"subset":[61],"selection":[62],"done":[64],"enhance":[66],"performance.":[68],"classifier":[70,106],"trained":[72],"with":[73,145,165],"known":[75],"input":[76],"achieve":[78],"targeted":[80],"output.":[81],"ground":[83],"truth":[84],"classification":[87],"provided":[89],"by":[90],"an":[91,166],"expert":[92],"observer.":[93],"method":[95,142,159],"overcomes":[96],"necessity":[98],"manual":[100],"intervention":[101],"required":[102],"training":[104,135],"used":[107],"previous":[110],"conventional":[111],"methods.":[112],"network":[117],"adjusts":[118],"weights":[120],"minimizes":[122],"mean":[124],"square":[125],"error":[126],"overcome":[128],"tedious":[130],"process":[131],"selecting":[133],"data":[136],"train":[138],"classifier.":[140],"was":[143],"investigated":[144],"365":[146],"from":[149],"15":[150],"examinations.":[152],"performance":[154],"index":[155],"proposed":[158],"measured":[161],"be":[163],"good":[164],"area":[167],"0.98":[168],"under":[169],"ROC":[170],"curve.":[171]},"counts_by_year":[],"updated_date":"2026-10-13T10:08:12.033832","created_date":"2017-11-10T00:00:00"}
