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Accurate and efficient diagnosis of skin rosacea is crucial in dermatological healthcare, yet remains challenging due to the need for precise classification and interpretability. This study aims to develop a robust and explainable approach for skin rosacea classification using advanced deep learning and explainable artificial intelligence (XAI) techniques. Our innovation lies in combining Swin Transformer models (Swin-Tiny, Swin-Small, and Swin-Base) with pre-trained convolutional neural network (CNN) architectures such as VGG19, ResNet50, and DenseNet201, addressing the limitations of traditional CNN-based methods. The proposed Swin-Base transformer achieves a state-of-the-art accuracy of 99%, outperforming all tested models across multiple evaluation metrics, including accuracy, precision, recall, F1 score, specificity, and others, on a dataset of 10,539 images. To enhance interpretability, we employ XAI techniques, including Score-CAM, GradCAM, and GradCAM++, which provide visual explanations and insights into the decision-making process. These findings underscore the potential of integrating cutting-edge transformer architectures with explainability techniques to build reliable, interpretable, and high-performing models for clinical dermatology, ultimately improving patient care.
Anjali et al. (Sat,) studied this question.