Comparison table of the published paper.
<div><p>Skin cancer (SC) is the most prominent form of cancer in humans, with over 1 million new cases reported worldwide each year. Early identification of SC plays a crucial role in effective treatment. However, protecting patient data privacy is a major concern in medical research. Th...
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2025
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| _version_ | 1852018428018163712 |
|---|---|
| author | Shuvo Biswas (21728782) |
| author2 | Sajeeb Saha (15840170) Muhammad Shahin Uddin (21728785) Rafid Mostafiz (21728788) |
| author2_role | author author author |
| author_facet | Shuvo Biswas (21728782) Sajeeb Saha (15840170) Muhammad Shahin Uddin (21728785) Rafid Mostafiz (21728788) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shuvo Biswas (21728782) Sajeeb Saha (15840170) Muhammad Shahin Uddin (21728785) Rafid Mostafiz (21728788) |
| dc.date.none.fl_str_mv | 2025-07-16T17:37:03Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0324393.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Comparison_table_of_the_published_paper_/29584891 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genetics Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified inceptionresnetv2 &# 8212 ensure model interpretability vgg16 algorithm showed maintain data privacy densenet169 algorithm obtained proposed framework offers proposed framework smart framework presented framework two well study presents several techniques prominent form medical research major concern known datasets first preprocessed effective treatment early identification dependable tool crucial role classification tasks best results agnostic explanations |
| dc.title.none.fl_str_mv | Comparison table of the published paper. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Skin cancer (SC) is the most prominent form of cancer in humans, with over 1 million new cases reported worldwide each year. Early identification of SC plays a crucial role in effective treatment. However, protecting patient data privacy is a major concern in medical research. Therefore, this study presents a smart framework for classifying SC leveraging deep learning (DL), federated learning (FL) and explainable AI (XAI). We tested the presented framework on two well-known datasets, ISBI2016 and ISBI2017. The data was first preprocessed by several techniques: resizing, normalization, balancing, and augmentation. Six advanced DL algorithms—VGG16, Xception, DenseNet169, InceptionV3, MobileViT, and InceptionResNetV2—were applied for classification tasks. Among these, the DenseNet169 algorithm obtained the highest accuracy of 83.3% in ISBI2016 and 92.67% in ISBI2017. All models were then tested in an FL platform to maintain data privacy. In the FL platform, the VGG16 algorithm showed the best results, with 92.08% accuracy on ISBI2016 and 94% on ISBI2017. To ensure model interpretability, an XAI-based algorithm named Local Interpretable Model-Agnostic Explanations (LIME) was used to explain the predictions of the proposed framework. We believe the proposed framework offers a dependable tool for SC diagnosis while protecting sensitive medical data.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_e43d59e31bcae5fbdf6bf7a0fc1de076 |
| identifier_str_mv | 10.1371/journal.pone.0324393.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29584891 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison table of the published paper.Shuvo Biswas (21728782)Sajeeb Saha (15840170)Muhammad Shahin Uddin (21728785)Rafid Mostafiz (21728788)GeneticsScience PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedinceptionresnetv2 &# 8212ensure model interpretabilityvgg16 algorithm showedmaintain data privacydensenet169 algorithm obtainedproposed framework offersproposed frameworksmart frameworkpresented frameworktwo wellstudy presentsseveral techniquesprominent formmedical researchmajor concernknown datasetsfirst preprocessedeffective treatmentearly identificationdependable toolcrucial roleclassification tasksbest resultsagnostic explanations<div><p>Skin cancer (SC) is the most prominent form of cancer in humans, with over 1 million new cases reported worldwide each year. Early identification of SC plays a crucial role in effective treatment. However, protecting patient data privacy is a major concern in medical research. Therefore, this study presents a smart framework for classifying SC leveraging deep learning (DL), federated learning (FL) and explainable AI (XAI). We tested the presented framework on two well-known datasets, ISBI2016 and ISBI2017. The data was first preprocessed by several techniques: resizing, normalization, balancing, and augmentation. Six advanced DL algorithms—VGG16, Xception, DenseNet169, InceptionV3, MobileViT, and InceptionResNetV2—were applied for classification tasks. Among these, the DenseNet169 algorithm obtained the highest accuracy of 83.3% in ISBI2016 and 92.67% in ISBI2017. All models were then tested in an FL platform to maintain data privacy. In the FL platform, the VGG16 algorithm showed the best results, with 92.08% accuracy on ISBI2016 and 94% on ISBI2017. To ensure model interpretability, an XAI-based algorithm named Local Interpretable Model-Agnostic Explanations (LIME) was used to explain the predictions of the proposed framework. We believe the proposed framework offers a dependable tool for SC diagnosis while protecting sensitive medical data.</p></div>2025-07-16T17:37:03ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0324393.t001https://figshare.com/articles/dataset/Comparison_table_of_the_published_paper_/29584891CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/295848912025-07-16T17:37:03Z |
| spellingShingle | Comparison table of the published paper. Shuvo Biswas (21728782) Genetics Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified inceptionresnetv2 &# 8212 ensure model interpretability vgg16 algorithm showed maintain data privacy densenet169 algorithm obtained proposed framework offers proposed framework smart framework presented framework two well study presents several techniques prominent form medical research major concern known datasets first preprocessed effective treatment early identification dependable tool crucial role classification tasks best results agnostic explanations |
| status_str | publishedVersion |
| title | Comparison table of the published paper. |
| title_full | Comparison table of the published paper. |
| title_fullStr | Comparison table of the published paper. |
| title_full_unstemmed | Comparison table of the published paper. |
| title_short | Comparison table of the published paper. |
| title_sort | Comparison table of the published paper. |
| topic | Genetics Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified inceptionresnetv2 &# 8212 ensure model interpretability vgg16 algorithm showed maintain data privacy densenet169 algorithm obtained proposed framework offers proposed framework smart framework presented framework two well study presents several techniques prominent form medical research major concern known datasets first preprocessed effective treatment early identification dependable tool crucial role classification tasks best results agnostic explanations |