Towards unbiased skin cancer classification using deep feature fusion
Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing network width augmentation to enh...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02889-w |
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| author | Ali Atshan Abdulredah Mohammed A. Fadhel Laith Alzubaidi Ye Duan Monji Kherallah Faiza Charfi |
| author_facet | Ali Atshan Abdulredah Mohammed A. Fadhel Laith Alzubaidi Ye Duan Monji Kherallah Faiza Charfi |
| author_sort | Ali Atshan Abdulredah |
| collection | DOAJ |
| description | Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing network width augmentation to enhance efficiency. The proposed model addresses potential biases associated with skin conditions, particularly in individuals with darker skin tones or excessive hair, by incorporating feature fusion to assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets to evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, and Melanoma Skin Cancer-comprising skin cancer images categorized into benign and malignant classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, were employed to enhance the interpretability of the model’s decisions. Comparative analysis was performed with three pre-existing deep learning networks-EfficientNet, MobileNet, and Darknet. The results demonstrate SWNet’s superiority, achieving an accuracy of 99.86% and an F1 score of 99.95%, underscoring its efficacy in gradient propagation and feature capture across various levels. This research highlights the significant potential of SWNet in advancing skin cancer detection and classification, providing a robust tool for accurate and early diagnosis. The integration of feature fusion enhances accuracy and mitigates biases associated with hair and skin tones. The outcomes of this study contribute to improved patient outcomes and healthcare practices, showcasing SWNet’s exceptional capabilities in skin cancer detection and classification. |
| format | Article |
| id | doaj-art-b607f93c00074ca8969d59bdde0f0d18 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-b607f93c00074ca8969d59bdde0f0d182025-02-02T12:27:50ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125112210.1186/s12911-025-02889-wTowards unbiased skin cancer classification using deep feature fusionAli Atshan Abdulredah0Mohammed A. Fadhel1Laith Alzubaidi2Ye Duan3Monji Kherallah4Faiza Charfi5National School of Electronics and Telecoms of Sfax, University of SfaxCollege of Computer Science and Information Technology, University of SumerSchool of Mechanical, Medical, and Process Engineering, Queensland University of TechnologySchool of Computing, Clemson UniversityFaculty of Science of Sfax, University of SfaxFaculty of Science of Sfax, University of SfaxAbstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing network width augmentation to enhance efficiency. The proposed model addresses potential biases associated with skin conditions, particularly in individuals with darker skin tones or excessive hair, by incorporating feature fusion to assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets to evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, and Melanoma Skin Cancer-comprising skin cancer images categorized into benign and malignant classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, were employed to enhance the interpretability of the model’s decisions. Comparative analysis was performed with three pre-existing deep learning networks-EfficientNet, MobileNet, and Darknet. The results demonstrate SWNet’s superiority, achieving an accuracy of 99.86% and an F1 score of 99.95%, underscoring its efficacy in gradient propagation and feature capture across various levels. This research highlights the significant potential of SWNet in advancing skin cancer detection and classification, providing a robust tool for accurate and early diagnosis. The integration of feature fusion enhances accuracy and mitigates biases associated with hair and skin tones. The outcomes of this study contribute to improved patient outcomes and healthcare practices, showcasing SWNet’s exceptional capabilities in skin cancer detection and classification.https://doi.org/10.1186/s12911-025-02889-wDeep learningSkin cancer classificationTransfer learningExplainable AIGrad-CAMFeature fusion |
| spellingShingle | Ali Atshan Abdulredah Mohammed A. Fadhel Laith Alzubaidi Ye Duan Monji Kherallah Faiza Charfi Towards unbiased skin cancer classification using deep feature fusion BMC Medical Informatics and Decision Making Deep learning Skin cancer classification Transfer learning Explainable AI Grad-CAM Feature fusion |
| title | Towards unbiased skin cancer classification using deep feature fusion |
| title_full | Towards unbiased skin cancer classification using deep feature fusion |
| title_fullStr | Towards unbiased skin cancer classification using deep feature fusion |
| title_full_unstemmed | Towards unbiased skin cancer classification using deep feature fusion |
| title_short | Towards unbiased skin cancer classification using deep feature fusion |
| title_sort | towards unbiased skin cancer classification using deep feature fusion |
| topic | Deep learning Skin cancer classification Transfer learning Explainable AI Grad-CAM Feature fusion |
| url | https://doi.org/10.1186/s12911-025-02889-w |
| work_keys_str_mv | AT aliatshanabdulredah towardsunbiasedskincancerclassificationusingdeepfeaturefusion AT mohammedafadhel towardsunbiasedskincancerclassificationusingdeepfeaturefusion AT laithalzubaidi towardsunbiasedskincancerclassificationusingdeepfeaturefusion AT yeduan towardsunbiasedskincancerclassificationusingdeepfeaturefusion AT monjikherallah towardsunbiasedskincancerclassificationusingdeepfeaturefusion AT faizacharfi towardsunbiasedskincancerclassificationusingdeepfeaturefusion |