SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare

Abstract

Background: Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional AI models often struggle with balancing accuracy and interpretability, which are critical for clinical adoption. Methods: The SmartSkin-XAI methodology incorporates a fine-tuned DenseNet121 model combined with XAI techniques to interpret predictions. This approach improves early detection and patient management by offering a transparent decision-making process. The model was evaluated using two datasets: the ISIC dataset and the Kaggle dataset. Performance metrics such as classification accuracy, precision, recall, and F1 score were compared against benchmark models, including DenseNet121, InceptionV3, and esNet50. Results: SmartSkin-XAI achieved a classification accuracy of 97% on the ISIC dataset and 98% on the Kaggle dataset. The model demonstrated high stability in precision, recall, and F1 score measures, outperforming the benchmark models. These results underscore the robustness and applicability of SmartSkin-XAI for real-world healthcare scenarios. Conclusions: SmartSkin-XAI addresses critical challenges in melanoma diagnosis by integrating state-of-the-art architecture with XAI methods, providing both accuracy and interpretability. This approach enhances clinical decision-making, fosters trust among healthcare professionals, and represents a significant advancement in incorporating AI-driven diagnostics into medicine, particularly for bedside applications.

Keywords

• melanoma detection
• explainable AI
• deep learning
• DenseNet
• SmartSkin-XAI