Explainable deep learning approaches for skin cancer diagnosis

Explainable deep learning approaches for skin cancer diagnosis

연구 분야: Artificial Intelligence

학회: Network Modeling Analysis in Health Informatics and Bioinformatics

Skin cancer continues to be a significant worldwide health issue, highlighting the critical need for precise and timely detection to guarantee positive patient outcomes and efficient treatment. While deep learning algorithms have demonstrated significant potential, they are frequently observed as black-box models, posing challenges for dermatologists in interpreting and validating their decisions. This study integrates deep learning with explainable artificial intelligence to address the complexities inherent in skin cancer detection. Four advanced pre-trained models—InceptionV3, Xception, ResNet50V2, and DenseNet121—are employed for the classification of skin lesions. To deal with the class imbalance and to improve the generalisation of the model, image augmentation techniques are considered. Transparency in the decision-making process is achieved through XAI, which is essential in medical contexts where interpretability fosters trust and supports the seamless adoption of AI-driven diagnostic systems in clinical workflows. Extensive evaluation reveals that better performances are obtained when Xception model is considered, achieving an accuracy of 90.15%, precision of 90.02%, recall of 90.25%, and an F1 score of 90.10%. These results highlight the transformative impact of integrating deep learning and XAI in skin cancer diagnosis, laying the foundation for future advancements in medical image analysis. The capacity of these technologies to enable early and accurate detection holds substantial promise for improving patient care, reducing healthcare costs, and increasing survival rates among individuals affected by skin cancer.