{"id":"https://openalex.org/W4410539003","doi":"https://doi.org/10.1109/ssd64182.2025.10989974","title":"AttCDCNet: Attention-Enhanced Chest Disease Classification Using X-Ray Images","display_name":"AttCDCNet: Attention-Enhanced Chest Disease Classification Using X-Ray Images","publication_year":2025,"publication_date":"2025-02-17","ids":{"openalex":"https://openalex.org/W4410539003","doi":"https://doi.org/10.1109/ssd64182.2025.10989974"},"language":"en","primary_location":{"id":"doi:10.1109/ssd64182.2025.10989974","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssd64182.2025.10989974","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD)","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/A5048077859","display_name":"Omar H. Khater","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Omar H. Khater","raw_affiliation_strings":["Computer Engineering Department"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Engineering Department","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061745537","display_name":"Abdullahi Sani Shuaibu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdullahi S. Shuaibu","raw_affiliation_strings":["Computer Engineering Department"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Engineering Department","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066858944","display_name":"Sami Ul Haq","orcid":"https://orcid.org/0009-0004-5488-0792"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sami Ul Haq","raw_affiliation_strings":["Computer Engineering Department"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Engineering Department","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049326529","display_name":"Abdul Jabbar Siddiqui","orcid":"https://orcid.org/0000-0001-6233-598X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdul Jabbar Siddiqui","raw_affiliation_strings":["SDAIA-KFUPM Joint Research Center for Artificial Intelligence, KFUPM,Computer Engineering Department"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SDAIA-KFUPM Joint Research Center for Artificial Intelligence, KFUPM,Computer Engineering Department","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.3305,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.94394323,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"891","last_page":"896"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9977999925613403,"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":0.9977999925613403,"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":0.9846000075340271,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9761000275611877,"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/computer-science","display_name":"Computer science","score":0.6266388297080994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48553037643432617},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.44843122363090515},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3699652850627899},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3472365736961365},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2523212432861328},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.1361144483089447}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6266388297080994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48553037643432617},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.44843122363090515},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3699652850627899},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3472365736961365},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2523212432861328},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.1361144483089447}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssd64182.2025.10989974","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssd64182.2025.10989974","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2962858109","https://openalex.org/W2963446712","https://openalex.org/W2963923251","https://openalex.org/W3021001507","https://openalex.org/W3155091372","https://openalex.org/W3182566493","https://openalex.org/W3185654650","https://openalex.org/W3200633813","https://openalex.org/W4205623122","https://openalex.org/W4308160156","https://openalex.org/W4309689151","https://openalex.org/W4317812149","https://openalex.org/W4366205113","https://openalex.org/W4383097728","https://openalex.org/W4385625617","https://openalex.org/W4390993455","https://openalex.org/W4392207614","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Chest":[0],"X-rays":[1],"(X-ray":[2],"images)":[3],"have":[4,49,56],"been":[5],"proven":[6],"to":[7,35,64,109,127,139,158,213],"be":[8],"effective":[9],"for":[10,28,92],"the":[11,40,45,58,66,73,93,100,111,115,120,129,132,141,146,176,183,206],"diagnosis":[12,29,67],"of":[13,60,75,95,131,199],"chest":[14],"diseases,":[15],"including":[16,166],"Pneumonia,":[17],"Lung":[18],"Opacity,":[19],"and":[20,37,135,169,186,197,202,217],"COVID-19.":[21],"However,":[22],"relying":[23],"on":[24,114,205],"traditional":[25],"medical":[26,41,163,221],"methods":[27],"from":[30],"X-ray":[31,46,96],"images":[32,47],"is":[33,72],"prone":[34],"delays":[36],"inaccuracies":[38],"because":[39],"personnel":[42],"who":[43],"evaluate":[44],"may":[48],"preconceived":[50],"biases.":[51],"For":[52],"this":[53,82],"reason,":[54],"researchers":[55],"proposed":[57,149,177,191],"use":[59,74],"deep":[61],"learning-based":[62],"techniques":[63],"facilitate":[65],"process.":[68],"The":[69,148,190],"preeminent":[70],"method":[71],"sophisticated":[76],"Convolutional":[77],"Neural":[78],"Networks":[79],"(CNNs).":[80],"In":[81],"work,":[83],"we":[84],"introduce":[85],"an":[86,106,194],"innovative":[87],"detection":[88],"model":[89,103,112,142,150,178,192],"named":[90],"AttCDCNet":[91],"task":[94],"image":[97,164],"diagnosis,":[98],"enhancing":[99],"popular":[101],"DenseNet121":[102,185],"by":[104,144,153],"adding":[105],"attention":[107],"block":[108],"help":[110],"focus":[113],"most":[116],"relevant":[117],"regions,":[118],"utilizing":[119,136],"focal":[121],"loss":[122,125],"as":[123],"a":[124],"function":[126],"overcome":[128],"imbalance":[130,168],"dataset":[133,167],"problem,":[134],"depth-wise":[137],"convolution":[138],"make":[140],"lighter":[143],"reducing":[145],"parameters.":[147],"demonstrates":[151,179],"novelty":[152],"effectively":[154],"integrating":[155],"these":[156],"enhancements":[157],"address":[159],"critical":[160],"challenges":[161],"in":[162,219],"classification,":[165],"computational":[170],"efficiency.":[171],"Through":[172],"extensive":[173],"experimental":[174],"evaluations,":[175],"exceptional":[180],"performance,":[181],"surpassing":[182],"original":[184],"other":[187],"state-of-the-art":[188],"models.":[189],"achieved":[193],"accuracy,":[195],"precision":[196],"recall":[198],"94.94%,":[200],"95.14%":[201],"94.53%,":[203],"respectively,":[204],"COVID-19":[207],"Radiography":[208],"Dataset.":[209],"Underscoring":[210],"its":[211],"potential":[212],"improve":[214],"diagnostic":[215],"accuracy":[216],"efficiency":[218],"real-world":[220],"applications.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}