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The proposed correction allows to refine the surface temperature determined by a standard noninvasive thermographic method based on IR images for the human skin with a nonuniform in depth temperature distribution. The nonuniformity is due to the biophysical necessity of normal functioning of the organism, as well as to a possible presence of pathological processes in certain areas of the body. The measured brightness can be spectral or polychromatic. The value of the correction is analyzed depending on the size, shape, and thermal power of the source of pathology, as well as the depth of its location, thermophysical and optical characteristics of the biological environment.

We have analyzed the feasibility and expediency of using a probabilistic neural network as a classifier for processing and interpreting data obtained using a biosensor we have developed that is based on whispering-gallery-mode optical resonance. We have established that the probabilistic neural network makes it possible to correctly classify the biological compounds under study with average 97.3% probability.