Precision Skin Tumor Diagnostics with machine learning and hyperspectral imaging

We propose a machine learning framework in which neural network models are trained and validated using hyperspectral tumor imaging data. These models are integrated with segmentation algorithms to accurately predict the tumor’s actual size and determine the optimal amount of tissue to remove. Beyond improving surgical precision, our approach also holds the potential to accurately classify tumor types. Initial results have been published, with further studies underway.