Enhancing the Accuracy of Skin Lesion Diagnosis Using Hyperspectral Imaging and Deep Learning

Abstract

This study presents a novel diagnostic approach that integrates hyperspectral imaging (HSI) with deep learning to discriminate among dermatitis, actinic keratosis (AK), and seborrheic keratosis (SK). We evaluated 60 intraoperative clinical specimens and achieved 93% accuracy, 91% sensitivity, and 95% specificity in three-class classification. A Savitzky–Golay filter was applied to the raw spectra to enhance the signal-to-noise ratio and data fidelity, while first-derivative spectral analysis enabled the model to capture subtle biochemical and morphological differences among lesions. Our results demonstrate that the combined HSI–deep-learning framework can accelerate dermatologic diagnosis and reduce error rates. This methodology not only provides a robust tool for clinical decision support in dermatology but also holds promise for wider adoption across medical imaging workflows. Future work will focus on scalability, cost–benefit optimization, and seamless integration with existing diagnostic platforms.