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<p>Skin cancer represents a major global health concern, driving significant research efforts toward improving diagnostic accuracy and treatment outcomes. Early detection remains essential for effective management, as it considerably enhances the chances of successful intervention. With the rising incidence of skin cancer worldwide, there is a critical need for reliable and interpretable diagnostic systems that can support clinicians in making timely and accurate decisions. This study investigates the application of deep learning (DL) models for skin lesion classification, focusing on three convolutional neural network (CNN) architectures: VGG16, DenseNet169, and DenseNet201. The models are trained to classify lesions as benign or malignant, aiming to strengthen early detection capabilities. Comparative analysis shows that all models deliver competitive results, with DenseNet201 achieving the best performance, recording an accuracy of 0.8788, a precision of 0.8776, a recall of 0.8781, and an F1 score of 0.8778. These results surpass several state-of-the-art methods, highlighting DenseNet201 as a robust approach for skin lesion classification. To improve the reliability and interpretability of the outcomes, the Integrated Gradients (IG) method is employed to explain the models’ decision-making process. This explainable artificial intelligence (XAI) technique identifies the most influential features contributing to classification, thereby increasing trust in the predictions and supporting evidence-based clinical practice. The findings emphasize the potential of CNN models, particularly DenseNet201, to advance computer-aided diagnosis (CAD) of skin cancer. By combining high predictive performance with interpretability, such models offer valuable tools to assist healthcare professionals in the early and accurate detection of malignant lesions.</p>

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Explainable transfer learning for skin cancer diagnosis

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Abdelkader Alrabai,
  • Amira Echtioui,
  • Fathi Kallel

摘要

Skin cancer represents a major global health concern, driving significant research efforts toward improving diagnostic accuracy and treatment outcomes. Early detection remains essential for effective management, as it considerably enhances the chances of successful intervention. With the rising incidence of skin cancer worldwide, there is a critical need for reliable and interpretable diagnostic systems that can support clinicians in making timely and accurate decisions. This study investigates the application of deep learning (DL) models for skin lesion classification, focusing on three convolutional neural network (CNN) architectures: VGG16, DenseNet169, and DenseNet201. The models are trained to classify lesions as benign or malignant, aiming to strengthen early detection capabilities. Comparative analysis shows that all models deliver competitive results, with DenseNet201 achieving the best performance, recording an accuracy of 0.8788, a precision of 0.8776, a recall of 0.8781, and an F1 score of 0.8778. These results surpass several state-of-the-art methods, highlighting DenseNet201 as a robust approach for skin lesion classification. To improve the reliability and interpretability of the outcomes, the Integrated Gradients (IG) method is employed to explain the models’ decision-making process. This explainable artificial intelligence (XAI) technique identifies the most influential features contributing to classification, thereby increasing trust in the predictions and supporting evidence-based clinical practice. The findings emphasize the potential of CNN models, particularly DenseNet201, to advance computer-aided diagnosis (CAD) of skin cancer. By combining high predictive performance with interpretability, such models offer valuable tools to assist healthcare professionals in the early and accurate detection of malignant lesions.