An explainable and federated deep learning framework for skin cancer diagnosis.
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0324393 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850096536953815040 |
|---|---|
| author | Shuvo Biswas Sajeeb Saha Muhammad Shahin Uddin Rafid Mostafiz |
| author_facet | Shuvo Biswas Sajeeb Saha Muhammad Shahin Uddin Rafid Mostafiz |
| author_sort | Shuvo Biswas |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-157581da47d84d29ae5b1dfca87171fb |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-157581da47d84d29ae5b1dfca87171fb2025-08-20T02:41:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032439310.1371/journal.pone.0324393An explainable and federated deep learning framework for skin cancer diagnosis.Shuvo BiswasSajeeb SahaMuhammad Shahin UddinRafid MostafizSkin 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.https://doi.org/10.1371/journal.pone.0324393 |
| spellingShingle | Shuvo Biswas Sajeeb Saha Muhammad Shahin Uddin Rafid Mostafiz An explainable and federated deep learning framework for skin cancer diagnosis. PLoS ONE |
| title | An explainable and federated deep learning framework for skin cancer diagnosis. |
| title_full | An explainable and federated deep learning framework for skin cancer diagnosis. |
| title_fullStr | An explainable and federated deep learning framework for skin cancer diagnosis. |
| title_full_unstemmed | An explainable and federated deep learning framework for skin cancer diagnosis. |
| title_short | An explainable and federated deep learning framework for skin cancer diagnosis. |
| title_sort | explainable and federated deep learning framework for skin cancer diagnosis |
| url | https://doi.org/10.1371/journal.pone.0324393 |
| work_keys_str_mv | AT shuvobiswas anexplainableandfederateddeeplearningframeworkforskincancerdiagnosis AT sajeebsaha anexplainableandfederateddeeplearningframeworkforskincancerdiagnosis AT muhammadshahinuddin anexplainableandfederateddeeplearningframeworkforskincancerdiagnosis AT rafidmostafiz anexplainableandfederateddeeplearningframeworkforskincancerdiagnosis AT shuvobiswas explainableandfederateddeeplearningframeworkforskincancerdiagnosis AT sajeebsaha explainableandfederateddeeplearningframeworkforskincancerdiagnosis AT muhammadshahinuddin explainableandfederateddeeplearningframeworkforskincancerdiagnosis AT rafidmostafiz explainableandfederateddeeplearningframeworkforskincancerdiagnosis |