Multidimensional Analysis of Dermoscopic Images and Spectral Information for the Diagnosis of Skin Tumors

The paper is devoted to the identification of skin tumors and interpretation of their component composition based on multidimensional analysis of Raman scattering spectral data and dermoscopic images. The dataset contains 65 samples of malignant melanomas, 107 seborrheic keratoses, and 166 nevi. The multivariate curve resolution alternating least squares analysis was performed for the Raman spectra to extract spectral profiles of main skin components and their composition. The obtained biochemical profiles of skin neoplasms were analyzed by the gradient boosting method. Dermoscopic image analysis was performed using a convolutional neural network with modified Visual Geometry Group 16-layer model architecture. Joint analysis of component and spatial features was carried out using logistic regression of the predicted values of a model based on Raman spectra and a model based on image analysis. As a result, a binary model for the classification of malignant melanoma and pigmented benign neoplasms was constructed, showing an area under the receiver operating characteristic of 0.94 (0.90−0.98) with 95% confidence interval. Combining spatial and Raman spectra derived component features makes it possible to increase the efficiency of diagnosing skin cancer.