A hybrid deep learning framework for skin disease localization and classification using wearable sensors.

ABSTRACT: Accurate detection of skin diseases is essential for timely intervention and treatment. This article proposes a patch-based, interpretable deep learning framework for skin disease detection using wearable sensors and clinical data. Specifically, a fully convolutional residual neural network (FCRN) is employed to extract local features from high-resolution skin images captured via wearable sensors, using a patch-level training approach. Pre-processing techniques-including image resampling, intensity normalization, and noise reduction-standardize the input data to ensure consistency across sensor variations. To enhance local feature learning, the FCRN incorporates residual modules, which mitigate gradient vanishing and improve model performance. The framework generates disease probabi

SUBMITTER: Zhao X 

PROVIDER: S-EPMC12453764 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature