Abstract: Accurate identification of diverse skin conditions is critical for effective patient care, particularly as Monkeypox (Mpox) has emerged as a significant global concern with an increasing number of cases worldwide. Individuals affected by Mpox often exhibit distinctive skin symptoms, and the disease carries a risk of transmission through contact, highlighting the need for reliable diagnostic systems. Existing studies in skin disease detection predominantly rely on single convolutional neural networks or traditional ensemble methods, which may struggle to generalize across diverse conditions. To address these limitations, we propose a novel fuzzy rank-based ensemble framework for the detection of multiple skin diseases. Our approach employs transfer learning to enhance the feature extraction capabilities of multiple base learners, including DenseNet169, MobileNetV2, and ResNet101V2, augmented with additional fully connected layers. Fuzzy logic principles are then applied to aggregate decision scores from these models, using two non-linear functions to adaptively weigh the confidence of each base learner for every input sample. We conduct a comprehensive evaluation on three datasets, comprising two Mpox datasets and one multi-class skin disease dataset, comparing our method against baseline models and recent state-of-the-art approaches. Results demonstrate that the proposed ensemble improves classification reliability and robustness, while interpretability analyses using Grad-CAM and LIME offer insights into model decision-making. Despite its effectiveness, the approach depends on dataset quality and diversity, and the inherent complexity of deep learning models may limit full interpretability in clinical scenarios. Overall, the proposed framework provides a confidence-aware, adaptable solution for accurate and reliable skin disease diagnosis.

Key words: Skin diseases')">Skin diseases, Ensemble learning, Transfer learning, Fuzzy rank-based fusion