{"id":"https://openalex.org/W7161962087","doi":"https://doi.org/10.1002/ima.70372","title":"BiOrthoNet : Efficient Retinal OCT Classification Using Direction\u2010Specific Convolutions","display_name":"BiOrthoNet : Efficient Retinal OCT Classification Using Direction\u2010Specific Convolutions","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7161962087","doi":"https://doi.org/10.1002/ima.70372"},"language":"en","primary_location":{"id":"doi:10.1002/ima.70372","doi":"https://doi.org/10.1002/ima.70372","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ima.70372","pdf_url":null,"source":{"id":"https://openalex.org/S15952048","display_name":"International Journal of Imaging Systems and Technology","issn_l":"0899-9457","issn":["0899-9457","1098-1098"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Imaging Systems and Technology","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1002/ima.70372","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134740012","display_name":"Ezzaldeen H.A. Abukhattab","orcid":"https://orcid.org/0009-0002-3573-1316","observed_orcids":["https://orcid.org/0009-0002-3573-1316"]},"institutions":[{"id":"https://openalex.org/I87673952","display_name":"Erciyes University","ror":"https://ror.org/047g8vk19","country_code":"TR","type":"education","lineage":["https://openalex.org/I87673952"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ezzaldeen H. A. Abukhattab","raw_affiliation_strings":["Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences Erciyes University  Kayseri T\u00fcrkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences Erciyes University  Kayseri T\u00fcrkiye","institution_ids":["https://openalex.org/I87673952"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041609091","display_name":"Turgay Batbat","orcid":"https://orcid.org/0000-0002-0128-2076","observed_orcids":["https://orcid.org/0000-0002-0128-2076"]},"institutions":[{"id":"https://openalex.org/I87673952","display_name":"Erciyes University","ror":"https://ror.org/047g8vk19","country_code":"TR","type":"education","lineage":["https://openalex.org/I87673952"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Turgay Batbat","raw_affiliation_strings":["Department of Biomedical Engineering, Engineering Faculty Erciyes University  Kayseri T\u00fcrkiye"],"raw_orcid":"https://orcid.org/0000-0002-0128-2076","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Engineering Faculty Erciyes University  Kayseri T\u00fcrkiye","institution_ids":["https://openalex.org/I87673952"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5041609091"],"corresponding_institution_ids":["https://openalex.org/I87673952"],"apc_list":{"value":3800,"currency":"USD","value_usd":3800},"apc_paid":{"value":3800,"currency":"USD","value_usd":3800},"fwci":4.6712,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.93488062,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"36","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9990000128746033,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9990000128746033,"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":0.00019999999494757503,"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/T11569","display_name":"Optical Coherence Tomography Applications","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/optical-coherence-tomography","display_name":"optical coherence tomography","score":0.9430000185966492},{"id":"https://openalex.org/keywords/retinal-disease-classification","display_name":"retinal disease classification","score":0.9890000224113464},{"id":"https://openalex.org/keywords/directional-feature-extraction","display_name":"directional feature extraction","score":0.9269999861717224},{"id":"https://openalex.org/keywords/lightweight-convolutional-neural-network","display_name":"lightweight convolutional neural network","score":0.7429999709129333},{"id":"https://openalex.org/keywords/retinal-anatomy","display_name":"retinal anatomy","score":0.7900000214576721},{"id":"https://openalex.org/keywords/saliency-maps","display_name":"saliency maps","score":0.7950000166893005},{"id":"https://openalex.org/keywords/low-latency-inference","display_name":"low-latency inference","score":0.8009999990463257},{"id":"https://openalex.org/keywords/neh","display_name":"NeH+","score":0.7639999985694885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.838699996471405},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5181000232696533},{"id":"https://openalex.org/C2778818243","wikidata":"https://www.wikidata.org/wiki/Q899552","display_name":"Optical coherence tomography","level":2,"score":0.5175999999046326},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C67953723","wikidata":"https://www.wikidata.org/wiki/Q192525","display_name":"Workstation","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4097999930381775},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4083999991416931},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34950000047683716},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2915000021457672},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/ima.70372","doi":"https://doi.org/10.1002/ima.70372","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ima.70372","pdf_url":null,"source":{"id":"https://openalex.org/S15952048","display_name":"International Journal of Imaging Systems and Technology","issn_l":"0899-9457","issn":["0899-9457","1098-1098"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Imaging Systems and Technology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1002/ima.70372","doi":"https://doi.org/10.1002/ima.70372","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ima.70372","pdf_url":null,"source":{"id":"https://openalex.org/S15952048","display_name":"International Journal of Imaging Systems and Technology","issn_l":"0899-9457","issn":["0899-9457","1098-1098"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Imaging Systems and Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2029628510","https://openalex.org/W2149933564","https://openalex.org/W2163605009","https://openalex.org/W2418802570","https://openalex.org/W2570893893","https://openalex.org/W2589435368","https://openalex.org/W2626778328","https://openalex.org/W2752782242","https://openalex.org/W2773475231","https://openalex.org/W2884585870","https://openalex.org/W2905493020","https://openalex.org/W2914016683","https://openalex.org/W2944779197","https://openalex.org/W3110529192","https://openalex.org/W3204892857","https://openalex.org/W4221020366","https://openalex.org/W4399564111","https://openalex.org/W4403037568","https://openalex.org/W4403342691","https://openalex.org/W4404371092","https://openalex.org/W4404441150","https://openalex.org/W4406792278","https://openalex.org/W4407077435","https://openalex.org/W4408139725","https://openalex.org/W4409125955","https://openalex.org/W4411107382","https://openalex.org/W4412658609","https://openalex.org/W4412732731","https://openalex.org/W4412984946"],"related_works":["https://openalex.org/W7156241982","https://openalex.org/W4416250704","https://openalex.org/W7172497228","https://openalex.org/W7219906413","https://openalex.org/W4409125955","https://openalex.org/W4406792278","https://openalex.org/W3211286865","https://openalex.org/W4411405440","https://openalex.org/W4296218651","https://openalex.org/W4416641381"],"abstract_inverted_index":{"ABSTRACT":[0],"Reliable":[1],"interpretation":[2],"of":[3,15,87,95,115,169,179],"optical":[4],"coherence":[5],"tomography":[6],"(OCT)":[7],"scans":[8],"is":[9,135,163],"crucial":[10],"for":[11,26,143,187,251],"the":[12,40,138,195,208],"early":[13],"detection":[14],"retinal":[16,47,65,204,254],"diseases.":[17],"Deep":[18],"learning":[19],"has":[20],"emerged":[21],"as":[22],"a":[23,54,92,147,152,165,223],"promising":[24],"approach":[25],"this":[27],"task;":[28],"however,":[29],"many":[30],"existing":[31],"models":[32],"are":[33],"computationally":[34],"intensive":[35],"and":[36,72,91,110,121,129,202,235,247],"fail":[37],"to":[38],"exploit":[39],"horizontal\u2010vertical":[41],"structural":[42,213],"organization":[43],"commonly":[44],"observed":[45],"in":[46,215],"OCT":[48,104,216],"images.":[49,217],"This":[50],"work":[51],"introduces":[52],"BiOrthoNet,":[53],"lightweight":[55],"convolutional":[56],"architecture":[57],"that":[58,77,207],"incorporates":[59],"directional":[60,197],"feature":[61,81],"extraction":[62],"inspired":[63],"by":[64],"anatomy":[66],"via":[67],"strictly":[68],"parallel":[69],"anisotropic":[70],"convolutions":[71],"an":[73,227,236,245],"efficient":[74,188,246],"BiOrthoAttention":[75],"module":[76],"adaptively":[78],"fuses":[79],"orthogonal":[80],"streams.":[82],"Despite":[83],"its":[84],"compact":[85],"size":[86],"2.24":[88],"million":[89],"parameters":[90],"computational":[93,192],"cost":[94],"1.93":[96],"GFLOPs,":[97],"BiOrthoNet":[98,243],"achieves":[99],"consistent":[100,212],"performance":[101],"across":[102],"multiple":[103],"benchmarks":[105],"with":[106,226],"varying":[107],"data":[108],"distributions":[109],"acquisition":[111],"conditions,":[112],"reporting":[113],"accuracies":[114],"99.79%,":[116],"98.00%,":[117],"97.82%,":[118],"94.16%,":[119],"92.75%,":[120],"92.24%":[122],"on":[123,222],"OCT2017,":[124],"UCSD,":[125],"OCT\u2010C8,":[126],"NEH,":[127],"OCTDL,":[128],"OCTID,":[130],"respectively.":[131],"The":[132,171],"OCT2017":[133],"accuracy":[134],"reported":[136],"under":[137,151,190],"dataset's":[139],"official":[140],"train/test":[141],"partition":[142],"benchmark":[144],"comparability,":[145],"while":[146],"supplementary":[148],"leakage\u2010free":[149],"evaluation":[150],"strict":[153],"global":[154],"patient\u2010level":[155],"re\u2010split":[156],"(five":[157],"random":[158],"seeds)":[159],"yielded":[160],"accuracy,":[161],"which":[162],"considered":[164],"more":[166],"reliable":[167],"estimate":[168],"generalization.":[170],"proposed":[172],"model":[173,209],"also":[174],"delivers":[175],"low\u2010latency":[176],"inference":[177,189],"speeds":[178],"5\u201314":[180],"ms":[181],"per":[182],"B\u2010scan,":[183],"making":[184],"it":[185],"suitable":[186],"limited":[191],"resources.":[193],"Furthermore,":[194],"generated":[196],"saliency":[198],"maps":[199],"highlight":[200],"horizontal":[201],"vertical":[203],"structures,":[205],"indicating":[206],"captures":[210],"directionally":[211],"patterns":[214],"All":[218],"experiments":[219],"were":[220],"conducted":[221],"workstation":[224],"equipped":[225],"NVIDIA":[228],"RTX":[229],"3050":[230],"GPU":[231],"(6":[232],"GB":[233],"VRAM)":[234],"AMD":[237],"Ryzen":[238],"7":[239],"5235HS":[240],"CPU.":[241],"Overall,":[242],"offers":[244],"anatomically":[248],"motivated":[249],"solution":[250],"automated":[252],"OCT\u2010based":[253],"disease":[255],"classification.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-05-22T00:00:00"}
