FIELD: medicine.
SUBSTANCE: invention relates to oncology, and can be used for diagnosing skin melanoma. Disclosed is a method for early automated remote diagnostics of skin melanoma consisting in performing digital photographs, computer-assisted skin membrane skin mapping with creation of a database of all detected pigmented skin growths, wherein melanoma suspicious foci are detected, characterized by analyzing initial images of suspicious skin areas, reducing images to size of 512 × 512 pixels, method includes automatic diagnosis of melanoma based on initial images of skin areas using a three-layer computer program such as "neural networks", pre-trained to distinguish melanoma skin based on reference images, comprising pre-processing device, automatically selecting based on analysis of Fourier spectrum initial images of essential features, allowing to divide these images into two classes corresponding to presence of diagnosis of skin melanoma or its absence; using this computer program, each reference image of the training sample is put in correspondence of the third layer neuron; in the transformed image space, the third layer neurons estimate the Euclidean distance from each reference image of the training sample to the image under test, wherein the derived evaluations are assigned a positive or negative sign depending on the class - presence or absence of a melanoma to which the reference image is assigned; among 70 neurons of the first layer in each of two classes "winners" are detected by minimum Euclidean distance from the reference image to the tested one; with help of 20 neurons of the second layer, inverse values of Euclidean distances, taken with the corresponding sign, are summed up in groups of "winners" and based on the sum comparison with the zero threshold value, the tested image class is determined, which corresponds to the skin melanoma diagnosis or to its absence.
EFFECT: invention provides higher probability of diagnosing skin melanoma, eliminating dependence of its quality on insufficient qualification of a specialist, automatic diagnostic mode, detection of high-risk groups of skin melanoma among the whole population.
1 cl, 1 dwg, 1 tbl
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Authors
Dates
2019-04-18—Published
2018-02-26—Filed