Manifold learning for dimensionality reduction and clustering of skin spectroscopy data

Diagnosis of benign and malign skin lesions is currently done mostly relying on visual assessment and frequent biopsies performed by dermatologists. As the timely and correct diagnosis of these skin lesions is one of the most important factors in the therapeutic outcome, leveraging new technologies to assist the dermatologist seems natural. Optical spectroscopy is a technology that is being established to aid skin lesion diagnosis, as the multi-spectral nature of this imaging method allows to detect multiple physiological changes like those associated with increased vasculature, cellular structure, oxygen consumption or edema in tumors. However, spectroscopy data is typically very high dimensional (on the order of thousands), which causes difficulties in visualization and classification. In this work we apply different manifold learning techniques to reduce the dimensions of the input data and get clustering results. Spectroscopic data of 48 patients with suspicious and actually malignant lesions was analyzed using ISOMAP, Laplacian Eigenmaps and Diffusion Maps with varying parameters and compared to results using PCA. Using optimal parameters, both ISOMAP and Laplacian Eigenmaps could cluster the data into suspicious and malignant with 96% accuracy, compared to the diagnosis of the treating physicians.