In the paper, we present test results of methods for the noninvasive diagnosis of skin neoplasms, based on the hyperspectral registration of images by using a camera with an acousto-optic tunable filter. For the identification of oncological pathologies, an integral spectral index has been proposed for a set of concentric regions around the source of neoplasm growth for the tissue sample under study. As well as taking account of changes in the spectral properties of the tissue, the introduced index indirectly takes into account classical ABCD dermatoscopic features: asymmetry, border irregularity, color diversity, and the tumor diameter. Results of training set separating are presented and the applicability of the proposed approaches to the clinical practice is shown.

The paper presents results of using a neural network classifier to analyze images of malignant skin lesions obtained using a hyper-spectral camera. Using a three-block neural network of VGG architecture, we conducted the classification of a set of two-dimensional images of melanoma, papilloma and basal cell carcinoma, obtained in the range of 530 – 570 and 600 – 606 nm, characterized by the highest absorption of melanin and hemoglobin. The sufficiency of the inclusion in the training set of two-dimensional images of a limited spectral range is analyzed. The results obtained show significant prospects of using neural network algorithms for processing hyperspectral data for the classification of skin pathologies. With a relatively small set of training data used in the study, the classification accuracy for the three types of neoplasms was as high as 96 %.

The optical coherence tomography (OCT) technique is applied in the diagnosis of the skin tissue. In general, quantitative imaging features obtained from OCT images have already been used as biomarkers to categorize skin tumors. Particularly, the fractal dimension (FD) could be capable of providing an efficient approach for analyzing OCT images of skin tumors. The 2D Fourier fractal analysis (FFA) as well as the differential box counting method (DBCM) was used in this paper to classify the basal cell carcinomas (BCC), melanomas, and benign melanocytic nevi. Generalized estimating equations were used to test for differences between skin tumors. Our results showed that the significant decrease of the 2D FD was detected in the benign melanocytic nevi and basal cell carcinomas as compared with the melanomas. Our results also suggested that the 2D FFA could provide a more efficient way to calculating FD to differentiate the basal cell carcinomas, melanomas, and benign melanocytic nevi as compared to the 2D DBCM.