{"id":"https://openalex.org/W4392152269","doi":"https://doi.org/10.1109/globecom54140.2023.10437676","title":"Pre-Trained Lightweight Deep Learning Models for Surgical Instrument Detection: Performance Evaluation for Edge Inference","display_name":"Pre-Trained Lightweight Deep Learning Models for Surgical Instrument Detection: Performance Evaluation for Edge Inference","publication_year":2023,"publication_date":"2023-12-04","ids":{"openalex":"https://openalex.org/W4392152269","doi":"https://doi.org/10.1109/globecom54140.2023.10437676"},"language":"en","primary_location":{"id":"doi:10.1109/globecom54140.2023.10437676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom54140.2023.10437676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2023 - 2023 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","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/A5064527361","display_name":"Md. Sabbir Ahmed","orcid":"https://orcid.org/0000-0002-4405-3986"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Md Sabbir Ahmed","raw_affiliation_strings":["University of Pisa,Department of Information Engineering,Pisa,Italy","Department of Information Engineering, University of Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pisa,Department of Information Engineering,Pisa,Italy","institution_ids":["https://openalex.org/I108290504"]},{"raw_affiliation_string":"Department of Information Engineering, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021026284","display_name":"Stefano Giordano","orcid":"https://orcid.org/0000-0002-1408-7528"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Stefano Giordano","raw_affiliation_strings":["University of Pisa,Department of Information Engineering,Pisa,Italy","Department of Information Engineering, University of Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pisa,Department of Information Engineering,Pisa,Italy","institution_ids":["https://openalex.org/I108290504"]},{"raw_affiliation_string":"Department of Information Engineering, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I108290504"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3873","last_page":"3878"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9962000250816345,"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"}},"topics":[{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9962000250816345,"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"}},{"id":"https://openalex.org/T10916","display_name":"Surgical Simulation and Training","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/computer-science","display_name":"Computer science","score":0.7240661382675171},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7193726301193237},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6127897500991821},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5626316070556641},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5192908644676208},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.411872535943985}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7240661382675171},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7193726301193237},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6127897500991821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5626316070556641},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5192908644676208},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.411872535943985}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/globecom54140.2023.10437676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom54140.2023.10437676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2023 - 2023 IEEE Global Communications Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:arpi.unipi.it:11568/1242208","is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1242208","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2106903015","https://openalex.org/W2262715205","https://openalex.org/W2271840356","https://openalex.org/W2419597278","https://openalex.org/W2580456502","https://openalex.org/W2592929672","https://openalex.org/W2792767783","https://openalex.org/W2893813411","https://openalex.org/W2905810301","https://openalex.org/W2955425717","https://openalex.org/W2960833983","https://openalex.org/W2979532137","https://openalex.org/W3012630231","https://openalex.org/W3026010264","https://openalex.org/W3083487428","https://openalex.org/W3087645054","https://openalex.org/W3134650884","https://openalex.org/W3175298934","https://openalex.org/W3196495614","https://openalex.org/W4200577878","https://openalex.org/W4214809079","https://openalex.org/W4280517006","https://openalex.org/W4285092498","https://openalex.org/W4297775537","https://openalex.org/W4319971326","https://openalex.org/W4321349613","https://openalex.org/W4321502015","https://openalex.org/W6694517276","https://openalex.org/W6717177883","https://openalex.org/W6737664043","https://openalex.org/W6762718338","https://openalex.org/W6774938788"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Surgical":[0],"instrument":[1,32,79,158],"detection":[2,33,159],"and":[3,12,23,58,71,106,111,134,145,181,187],"tracking":[4],"is":[5,18,61,83],"essential":[6],"in":[7,49],"surgical":[8,16,31,78,86,157],"procedures.":[9],"The":[10],"presence":[11],"proper":[13],"use":[14],"of":[15,147,170,185],"instruments":[17,87],"crucial":[19],"for":[20,41,74,113,120,155,161,175],"patient":[21,25],"safety":[22],"overall":[24],"outcomes.":[26],"Most":[27],"existing":[28],"deep":[29,75,102,172],"learning-based":[30],"systems":[34,45],"require":[35],"high-end":[36],"GPUs":[37],"or":[38,51],"cloud":[39],"services":[40],"deployment.":[42],"However,":[43],"such":[44],"are":[46,118],"not":[47],"feasible":[48],"resource-constrained":[50],"remote":[52],"areas":[53],"where":[54],"access":[55],"to":[56,84],"expensive":[57],"bulky":[59],"equipment":[60],"limited.":[62],"To":[63],"overcome":[64],"these":[65],"challenges,":[66],"we":[67],"propose":[68],"a":[69,94,138],"cost-effective":[70,188],"sustainable":[72],"solution":[73],"learning":[76,103,173],"based":[77],"detection.":[80],"Our":[81],"approach":[82],"detect":[85],"from":[88],"images":[89],"using":[90],"pretrained":[91,101],"models":[92,117,174],"on":[93,108,137,178],"Raspberry":[95],"Pi.":[96],"We":[97,127,141],"compare":[98],"two":[99],"lightweight,":[100],"models,":[104],"EfficientNet":[105],"MobileNet,":[107],"their":[109,153],"accuracy":[110,133],"efficiency":[112,136],"this":[114],"task.":[115],"These":[116],"optimized":[119],"edge":[121,162,179],"devices":[122],"with":[123],"limited":[124],"computational":[125,135],"resources.":[126],"evaluate":[128],"the":[129,143,168,183],"tradeoff":[130],"between":[131],"model":[132],"benchmark":[139],"dataset.":[140],"discuss":[142],"strengths":[144],"limitations":[146],"each":[148],"model,":[149],"as":[150,152],"well":[151],"implications":[154],"developing":[156],"applications":[160],"devices.":[163],"This":[164],"work":[165],"can":[166],"enable":[167],"selection":[169],"effective":[171],"real-time":[176],"inference":[177],"devices,":[180],"facilitate":[182],"development":[184],"efficient":[186],"healthcare":[189],"solutions.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}