Skin disease classification using transfer learning model and fusion strategy

Inflammatory skin diseases often display overlapping visual features, making accurate diagnosis challenging. This study proposes a deep learning framework combining transfer learning, feature fusion, and adaptive ensemble strategies to improve dermatological image classification. Using MobileNetV3-Large as the backbone, expert-defined anatomical metadata and model-derived probabilities were fused to enrich diagnostic features. A fuzzy rank-based ensemble aggregated predictions across multiple regions of interest (ROIs), prioritizing classifier confidence dynamically. The approach achieved consistent performance across ROI settings, with F1-scores reaching 0.8. These findings demonstrate that integrating anatomical context with deep learning enhances the interpretability and diagnostic utility of automated dermatological systems. • Inflammatory skin diseases have similar symptoms, leading to diagnostic challenges for dermatologists. • This research employs transfer learning and fusion techniques. • Fuzzy ranking integrates data from diverse ROI images and metadata. • Combining limited image data with anatomical knowledge enhances complex skin disease diagnosis accuracy.