Mobile multimodal optical imaging and transformer-based iterative fusion for differentiating seborrheic dermatitis and psoriasis

Seborrheic dermatitis and psoriasis are skin conditions that can adversely affect patients’ mental health and quality of life. Despite overlapping early-stage features, they differ in cause and treatment, making early and accurate diagnosis essential. To address this, we present a deep learning-based diagnostic solution combining a mobile multimodal imaging system with a Transformer-based Iterative Fusion Classification Model. This platform non-invasively captures multispectral, polarization, and fluorescence images of skin lesions, offering high-contrast views of tissue structure and composition. Unlike traditional CNNs, our Transformer architecture iteratively fuses data across modalities using attention mechanisms, enabling it to detect subtle differences between the two diseases. This leads to more accurate and robust classification. Clinical validation shows that our model outperforms conventional methods, demonstrating the system’s potential for accessible and precise mobile skin disease screening, ultimately supporting earlier intervention and improved patient outcomes.