{"id":"https://openalex.org/W4415178900","doi":"https://doi.org/10.1109/access.2025.3621553","title":"Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification","display_name":"Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415178900","doi":"https://doi.org/10.1109/access.2025.3621553"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3621553","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3621553","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3621553","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049448736","display_name":"Rama N. Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ruhaan Singh","raw_affiliation_strings":["Farragut High School, Knoxville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Farragut High School, Knoxville, TN, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044373160","display_name":"Sreelekha Guggilam","orcid":"https://orcid.org/0000-0002-7795-2945"},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sreelekha Guggilam","raw_affiliation_strings":["Department of Mathematics and Statistics, Texas A&#x0026;M University, Corpus Christi, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Texas A&#x0026;M University, Corpus Christi, TX, USA","institution_ids":["https://openalex.org/I96749437"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049448736"],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.44454252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"178148","last_page":"178159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.2248000055551529,"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"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.2248000055551529,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.21879999339580536,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.20020000636577606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8662999868392944},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5436999797821045},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5210000276565552},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5080999732017517},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46140000224113464},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4609000086784363},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43790000677108765},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4350999891757965}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8662999868392944},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8072999715805054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7168999910354614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6672000288963318},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5436999797821045},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5210000276565552},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5080999732017517},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4609000086784363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43790000677108765},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4277999997138977},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38909998536109924},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3621553","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3621553","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:607b83bb34054340ba85770b757bae64","is_oa":true,"landing_page_url":"https://doaj.org/article/607b83bb34054340ba85770b757bae64","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 178148-178159 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3621553","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3621553","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W22040386","https://openalex.org/W1995137594","https://openalex.org/W2045732268","https://openalex.org/W2057536936","https://openalex.org/W2062177968","https://openalex.org/W2069816479","https://openalex.org/W2132791018","https://openalex.org/W2148143831","https://openalex.org/W2296073425","https://openalex.org/W2502390809","https://openalex.org/W2529153069","https://openalex.org/W2788633781","https://openalex.org/W2887842788","https://openalex.org/W2954801320","https://openalex.org/W2979313212","https://openalex.org/W2981895326","https://openalex.org/W3048627609","https://openalex.org/W3123742938","https://openalex.org/W3127597934","https://openalex.org/W3142849873","https://openalex.org/W3168997536","https://openalex.org/W4285140661","https://openalex.org/W4309664066","https://openalex.org/W4317436377","https://openalex.org/W4320481312","https://openalex.org/W4385567975","https://openalex.org/W4405675931","https://openalex.org/W4407574887"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1,92],"models":[2,61,172],"have":[3,40],"shown":[4],"strong":[5],"performance":[6,89,126,199],"in":[7,103,154,200],"medical":[8,17,129,201],"image":[9,130,202],"diagnostics.":[10],"The":[11,94,122,138],"scarcity":[12],"of":[13,23,90,143,186],"data":[14],"for":[15,194],"certain":[16],"conditions,":[18],"coupled":[19],"with":[20,173],"the":[21,64,88,107,112,148,162,166,170],"presence":[22],"noisy,":[24],"mislabeled,":[25],"or":[26],"non-generalizable":[27],"images,":[28],"poses":[29],"a":[30,49,80,175,183],"significant":[31],"challenge":[32],"to":[33,43,84,99,105,115,156],"model":[34,196],"performance.":[35],"Several":[36],"data-efficient":[37],"training":[38,82,108,187],"strategies":[39],"been":[41],"proposed":[42],"address":[44],"these":[45],"constraints.":[46],"However,":[47],"developing":[48],"generalizable":[50],"difficulty":[51],"ranking":[52],"mechanism":[53],"that":[54],"works":[55],"across":[56],"diverse":[57],"domains,":[58],"datasets,":[59,152],"and":[60,86,119,145,150,158,198],"while":[62,110],"reducing":[63],"computational":[65],"tasks":[66],"still":[67],"remains":[68],"challenging.":[69],"In":[70],"this":[71],"research,":[72],"we":[73],"propose":[74],"Iterative":[75],"Misclassification":[76],"Error":[77],"Training":[78],"(IMET),":[79],"novel":[81],"technique":[83,140,168],"optimize":[85],"improve":[87],"deep":[91],"models.":[93,164],"IMET":[95,139,167],"approach":[96],"is":[97],"aimed":[98],"identify":[100],"misclassified":[101],"samples":[102],"order":[104],"streamline":[106],"process,":[109],"prioritizing":[111],"model\u2019s":[113],"attention":[114],"edge":[116],"case":[117],"scenarios":[118],"rare":[120],"outcomes.":[121],"paper":[123],"evaluates":[124],"IMET\u2019s":[125,192],"on":[127,147],"benchmark":[128,135,163,171],"classification":[131],"datasets":[132],"against":[133],"standard":[134],"ResNet":[136],"architectures.":[137],"achieved":[141],"accuracies":[142],"80.3%":[144],"90.2%":[146],"OCTMNIST":[149],"PneumoniaMNIST":[151],"respectively,":[153],"comparison":[155],"77.6%":[157],"88.6%":[159],"obtained":[160],"by":[161],"Additionally,":[165],"outperformed":[169],"both":[174],"significantly":[176],"lower":[177,184],"parameter":[178],"count":[179],"as":[180,182],"well":[181],"number":[185],"samples.":[188],"These":[189],"results":[190],"demonstrate":[191],"potential":[193],"enhancing":[195],"accuracy":[197],"analysis.":[203]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-15T00:00:00"}
