Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.

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Title: Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.
Authors: Arshad, Mehak1 (AUTHOR), Khan, Muhammad Attique1 (AUTHOR) attique.khan@ieee.org, Almujally, Nouf Abdullah2 (AUTHOR) naalmujally@pnu.edu.sa, Alasiry, Areej3 (AUTHOR) areej.alasiry@kku.edu.sa, Marzougui, Mehrez3 (AUTHOR) mhrez@kku.edu.sa, Nam, Yunyoung4 (AUTHOR) ynam@sch.ac.kr
Source: BMC Medical Informatics & Decision Making. 7/1/2025, Vol. 25 Issue 1, p1-22. 22p.
Database: Academic Search Ultimate
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  Data: Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.
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  Data: <searchLink fieldCode="JN" term="%22BMC+Medical+Informatics+%26+Decision+Making%22">BMC Medical Informatics & Decision Making</searchLink>. 7/1/2025, Vol. 25 Issue 1, p1-22. 22p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=186335809
RecordInfo BibRecord:
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        Value: 10.1186/s12911-025-03051-2
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      – Code: eng
        Text: English
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      – TitleFull: Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.
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            NameFull: Arshad, Mehak
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            NameFull: Khan, Muhammad Attique
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            NameFull: Almujally, Nouf Abdullah
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            NameFull: Alasiry, Areej
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            NameFull: Marzougui, Mehrez
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            NameFull: Nam, Yunyoung
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            – D: 01
              M: 07
              Text: 7/1/2025
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              Y: 2025
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