{"id":"https://openalex.org/W4414538847","doi":"https://doi.org/10.1109/icc52391.2025.11161724","title":"From Cloud to Edge: Evaluating CNNs for Lung Disease Classification for Resource-Constrained Settings","display_name":"From Cloud to Edge: Evaluating CNNs for Lung Disease Classification for Resource-Constrained Settings","publication_year":2025,"publication_date":"2025-06-08","ids":{"openalex":"https://openalex.org/W4414538847","doi":"https://doi.org/10.1109/icc52391.2025.11161724"},"language":"en","primary_location":{"id":"doi:10.1109/icc52391.2025.11161724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"},"type":"article","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":true,"raw_author_name":"Md Sabbir Ahmed","raw_affiliation_strings":["University of Pisa,Department of Information Engineering,Pisa,Italy"],"affiliations":[{"raw_affiliation_string":"University of Pisa,Department of Information Engineering,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"],"affiliations":[{"raw_affiliation_string":"University of Pisa,Department of Information Engineering,Pisa,Italy","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5064527361"],"corresponding_institution_ids":["https://openalex.org/I108290504"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.4068059,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3180","last_page":"3185"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.8460999727249146,"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":0.8460999727249146,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.7509999871253967,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7537000179290771},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6793000102043152},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6758999824523926},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6636000275611877},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5595999956130981},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.46480000019073486},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.45170000195503235}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7537000179290771},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.738099992275238},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6793000102043152},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6758999824523926},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6636000275611877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5662999749183655},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5184000134468079},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4296000003814697},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.34439998865127563},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30480000376701355}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icc52391.2025.11161724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"},{"id":"pmh:oai:arpi.unipi.it:11568/1345135","is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1345135","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2954996726","https://openalex.org/W3002683979","https://openalex.org/W3013547516","https://openalex.org/W3034426226","https://openalex.org/W4281384416","https://openalex.org/W4306377038","https://openalex.org/W4309028162","https://openalex.org/W4320525814","https://openalex.org/W4384158183","https://openalex.org/W4385700958","https://openalex.org/W4386219509","https://openalex.org/W4386249192","https://openalex.org/W4391933025","https://openalex.org/W4393948187","https://openalex.org/W4400092945"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"investigates":[2],"the":[3,89,96,110,138,162],"application":[4],"of":[5,98,140],"deep":[6],"convolutional":[7],"neural":[8],"networks":[9],"(CNNs)":[10],"for":[11,114,132,154,175],"lung":[12,151],"disease":[13,152],"classification":[14],"from":[15],"chest":[16],"X-ray":[17],"images,":[18],"with":[19,70,119,182],"a":[20,46],"focus":[21],"on":[22,27,45,88,101],"deployment":[23,177],"in":[24,67,148,178],"resourceconstrained":[25],"settings":[26,181],"low-power":[28],"edge":[29,133,167],"devices.":[30],"Six":[31],"state-of-the-art":[32],"CNN":[33],"architectures,":[34],"including":[35],"ResNet101,":[36],"DenseNet201,":[37],"MobileNetV3-Large,":[38],"EfficientNetV2-B0,":[39],"InceptionResNetV2,":[40],"and":[41,49,103,106,145,172],"Xception,":[42],"were":[43],"evaluated":[44],"base":[47],"dataset":[48,52],"an":[50],"augmented":[51,71],"to":[53,164],"assess":[54],"their":[55],"generalization":[56],"capacity.":[57],"Data":[58],"augmentation":[59],"showed":[60,80],"varied":[61],"impacts:":[62],"ResNet101":[63],"exhibited":[64],"significant":[65],"improvement":[66],"validation":[68],"accuracy":[69,118],"data,":[72],"indicating":[73],"enhanced":[74],"generalization,":[75],"while":[76],"models":[77,113,124],"like":[78],"InceptionResNetV2":[79],"limited":[81,183],"improvement,":[82],"suggesting":[83],"underfitting.":[84],"Additionally,":[85],"inference":[86,146],"performance":[87],"Raspberry":[90],"Pi":[91],"4":[92],"Model":[93],"B":[94],"demonstrated":[95],"impact":[97],"model":[99,143],"complexity":[100],"speed":[102],"latency.":[104],"MobileNetV3-Large":[105],"EfficientNetV2-B0":[107],"emerged":[108],"as":[109],"most":[111],"suitable":[112],"real-time":[115,176],"deployment,":[116],"balancing":[117],"computational":[120],"efficiency,":[121],"whereas":[122],"deeper":[123],"incurred":[125],"higher":[126],"latency,":[127],"making":[128],"them":[129],"less":[130],"practical":[131],"deployment.":[134],"These":[135],"findings":[136],"underscore":[137],"importance":[139],"data":[141],"augmentation,":[142],"selection,":[144],"efficiency":[147,174],"optimizing":[149],"CNN-based":[150],"classifiers":[153],"real-world":[155],"healthcare":[156],"applications.":[157],"Future":[158],"work":[159],"will":[160],"extend":[161],"evaluation":[163],"include":[165],"heterogeneous":[166],"devices,":[168],"further":[169],"exploring":[170],"scalability":[171],"energy":[173],"rural":[179],"clinical":[180],"resources.":[184]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}