{"id":"https://openalex.org/W7225823938","doi":"https://doi.org/10.47852/bonviewmedin620210440","title":"Recursive Pixel Partitioning and Multi-Stream Deep Learning for Annotation-Efficient Lung Disease Classification","display_name":"Recursive Pixel Partitioning and Multi-Stream Deep Learning for Annotation-Efficient Lung Disease Classification","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225823938","doi":"https://doi.org/10.47852/bonviewmedin620210440"},"language":"en","primary_location":{"id":"doi:10.47852/bonviewmedin620210440","doi":"https://doi.org/10.47852/bonviewmedin620210440","is_oa":true,"landing_page_url":"https://doi.org/10.47852/bonviewmedin620210440","pdf_url":"https://ojs.bonviewpress.com/index.php/MEDIN/article/download/10440/2309","source":{"id":"https://openalex.org/S5407045031","display_name":"Medinformatics","issn_l":"3029-1321","issn":["3029-1321"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["oarl"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medinformatics","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.bonviewpress.com/index.php/MEDIN/article/download/10440/2309","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066504115","display_name":"Talib Iqball","orcid":"https://orcid.org/0000-0002-7758-6666","observed_orcids":["https://orcid.org/0000-0002-7758-6666"]},"institutions":[{"id":"https://openalex.org/I135388327","display_name":"University of Kashmir","ror":"https://ror.org/032xfst36","country_code":"IN","type":"education","lineage":["https://openalex.org/I135388327"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Talib Iqball","raw_affiliation_strings":["Department of Computer Applications, University of Kashmir, India"],"raw_orcid":"https://orcid.org/0000-0002-7758-6666","affiliations":[{"raw_affiliation_string":"Department of Computer Applications, University of Kashmir, India","institution_ids":["https://openalex.org/I135388327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115700895","display_name":"Umar Bashir","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I145846420","display_name":"University of Jammu","ror":"https://ror.org/02retg991","country_code":"IN","type":"education","lineage":["https://openalex.org/I145846420"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Umar Bashir","raw_affiliation_strings":["Department of Computer Science and Information Technology, University of Jammu, India"],"raw_orcid":"https://orcid.org/0009-0004-1495-6422","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Technology, University of Jammu, India","institution_ids":["https://openalex.org/I145846420"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016132786","display_name":"Mohd Arif Wani","orcid":"https://orcid.org/0000-0002-4178-3588","observed_orcids":["https://orcid.org/0000-0002-4178-3588"]},"institutions":[{"id":"https://openalex.org/I135388327","display_name":"University of Kashmir","ror":"https://ror.org/032xfst36","country_code":"IN","type":"education","lineage":["https://openalex.org/I135388327"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Arif Wani","raw_affiliation_strings":["Department of Computer Applications, University of Kashmir, India"],"raw_orcid":"https://orcid.org/0000-0002-4178-3588","affiliations":[{"raw_affiliation_string":"Department of Computer Applications, University of Kashmir, India","institution_ids":["https://openalex.org/I135388327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5093070656","display_name":"Sheikh Umar Mushtaq","orcid":"https://orcid.org/0000-0002-3350-4446","observed_orcids":["https://orcid.org/0000-0002-3350-4446"]},"institutions":[{"id":"https://openalex.org/I110360157","display_name":"Lovely Professional University","ror":"https://ror.org/00et6q107","country_code":"IN","type":"education","lineage":["https://openalex.org/I110360157"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sheikh Umar Mushtaq","raw_affiliation_strings":["School of Computer Application, Lovely Professional University, India"],"raw_orcid":"https://orcid.org/0000-0002-3350-4446","affiliations":[{"raw_affiliation_string":"School of Computer Application, Lovely Professional University, India","institution_ids":["https://openalex.org/I110360157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"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":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"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":0.9994999766349792,"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":9.999999747378752e-05,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7505999803543091},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6958000063896179},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6664999723434448},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6108999848365784},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5138000249862671},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4722999930381775},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4059999883174896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32120001316070557},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.47852/bonviewmedin620210440","doi":"https://doi.org/10.47852/bonviewmedin620210440","is_oa":true,"landing_page_url":"https://doi.org/10.47852/bonviewmedin620210440","pdf_url":"https://ojs.bonviewpress.com/index.php/MEDIN/article/download/10440/2309","source":{"id":"https://openalex.org/S5407045031","display_name":"Medinformatics","issn_l":"3029-1321","issn":["3029-1321"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["oarl"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medinformatics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.47852/bonviewmedin620210440","doi":"https://doi.org/10.47852/bonviewmedin620210440","is_oa":true,"landing_page_url":"https://doi.org/10.47852/bonviewmedin620210440","pdf_url":"https://ojs.bonviewpress.com/index.php/MEDIN/article/download/10440/2309","source":{"id":"https://openalex.org/S5407045031","display_name":"Medinformatics","issn_l":"3029-1321","issn":["3029-1321"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["oarl"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medinformatics","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225823938.pdf","grobid_xml":"https://content.openalex.org/works/W7225823938.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W2745075853","https://openalex.org/W3013277995","https://openalex.org/W3017744567","https://openalex.org/W3097211536","https://openalex.org/W3139379779","https://openalex.org/W3194023229","https://openalex.org/W3198163615","https://openalex.org/W3199942699","https://openalex.org/W3211153712","https://openalex.org/W4200321086","https://openalex.org/W4210290258","https://openalex.org/W4224305663","https://openalex.org/W4293372980","https://openalex.org/W4298149760","https://openalex.org/W4317038430","https://openalex.org/W4319940243","https://openalex.org/W4376870309","https://openalex.org/W4393002189","https://openalex.org/W4404418405","https://openalex.org/W4405386486","https://openalex.org/W4407870509","https://openalex.org/W4409001180","https://openalex.org/W4410294981","https://openalex.org/W4410569923","https://openalex.org/W7163124384","https://openalex.org/W7166464400"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"lung":[1,17,36,217],"disease":[2,37],"classification":[3],"from":[4,107],"chest":[5],"X-ray":[6,114],"(CXR)":[7],"images":[8,45,99],"remains":[9],"challenging":[10],"in":[11,255,275],"annotation-scarce":[12],"clinical":[13],"settings":[14,213],"where":[15],"pixel-level":[16],"masks":[18],"are":[19,69,134,187],"unavailable.":[20],"We":[21],"propose":[22],"a":[23,93],"two-stage":[24],"framework":[25,85,205],"combining":[26],"recursive":[27],"pixel":[28],"partitioning":[29],"(RPP)":[30],"with":[31,79],"multi-stream":[32,144],"deep":[33],"learning":[34,73],"for":[35,136,210],"classification.":[38],"Unlike":[39],"anatomical":[40],"segmentation,":[41],"RPP":[42],"hierarchically":[43],"decomposes":[44],"into":[46],"structured":[47],"spatial":[48,59],"sub-regions":[49],"without":[50,214],"manual":[51],"masks,":[52],"enabling":[53],"multi-resolution":[54],"feature":[55,137],"extraction":[56],"while":[57,161],"preserving":[58],"organization.":[60],"Dedicated":[61],"streams":[62],"process":[63],"individual":[64],"partitions,":[65],"and":[66,104,112,116,123,132,175,192,200,264],"their":[67],"features":[68],"aggregated":[70],"through":[71],"collaborative":[72],"to":[74,246],"reduce":[75],"information":[76],"loss":[77],"associated":[78],"single-stream":[80,153],"convolutional":[81],"neural":[82],"networks.":[83],"The":[84,204,236,252],"is":[86,261,278],"evaluated":[87],"on":[88,183],"two":[89],"frontal":[90],"CXR":[91],"datasets:":[92],"balanced":[94],"three-class":[95,141],"dataset":[96,120,253],"of":[97,234,244],"1,088":[98],"(361":[100],"COVID-19,":[101],"365":[102],"Normal,":[103],"362":[105],"Pneumonia)":[106],"the":[108,117,140,143,151,168,179,259],"COVID-19":[109],"Radiography":[110],"Database":[111],"Chest":[113],"dataset,":[115,170],"Shenzhen":[118,169],"tuberculosis":[119,125],"(326":[121],"normal":[122],"326":[124],"images).":[126],"ImageNet-pretrained":[127],"InceptionV3,":[128],"MobileNetV2,":[129],"Xception,":[130],"ResNet101,":[131],"NASNetMobile":[133],"used":[135,254,274],"extraction.":[138],"On":[139,167],"task,":[142],"model":[145],"achieves":[146,172],"97.71%":[147],"test":[148],"accuracy,":[149],"exceeding":[150],"best":[152],"baseline":[154],"(MobileNetV2,":[155],"94.50%)":[156],"by":[157,197,258],"3.21":[158],"percentage":[159],"points":[160],"converging":[162],"approximately":[163],"four":[164],"times":[165],"faster.":[166],"it":[171],"89.3%":[173],"accuracy":[174],"84.9%":[176],"sensitivity,":[177],"outperforming":[178],"reported":[180],"segmentation-based":[181],"pipelines":[182],"this":[184,247,256,276],"split.":[185],"Results":[186],"validated":[188],"using":[189],"stratified":[190],"holdout":[191],"k-fold":[193],"cross":[194],"validation,":[195],"supported":[196],"per-class":[198],"metrics":[199],"an":[201,207],"ablation":[202],"study.":[203],"provides":[206],"annotation-efficient":[208],"alternative":[209],"low":[211],"resource":[212],"requiring":[215],"radiologist-drawn":[216],"masks.":[218],"Received:":[219],"18":[220],"May":[221],"2026":[222,227,232],"|":[223,228],"Revised:":[224],"24":[225],"August":[226],"Accepted:":[229],"16":[230],"October":[231],"Conflicts":[233],"Interest":[235],"authors":[237,260],"declare":[238],"that":[239],"they":[240],"have":[241],"no":[242],"conflicts":[243],"interest":[245],"work.":[248],"Data":[249,294],"Availability":[250],"Statement":[251,284],"study":[257,277],"publicly":[262],"available":[263,279],"can":[265],"be":[266],"accessed":[267],"at":[268,280],"https://www.kaggle.com/datasets/nih-chest-xrays/data.":[269],"Also,":[270],"another":[271],"data":[272],"source":[273],"https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.":[281],"Author":[282],"Contribution":[283],"Talib":[285],"Iqball:":[286],"Conceptualization,":[287,313],"Methodology,":[288,314],"Software,":[289,315],"Formal":[290,329],"analysis,":[291,330],"Investigation,":[292],"Resources,":[293,316],"curation,":[295],"Writing":[296,304,317,331,335],"\u2013":[297,305,318,332,336],"original":[298,333],"draft,":[299,334],"Visualization.":[300,309],"Umar":[301,326],"Bashir:":[302],"Validation,":[303,328],"review":[306,319,337],"&":[307,320,338],"editing,":[308,321,339],"M.":[310],"Arif":[311],"Wani:":[312],"Supervision,":[322],"Project":[323,341],"administration.":[324,342],"Sheikh":[325],"Mushtaq:":[327],"Visualization,":[340]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
