FIELD: medicine.
SUBSTANCE: images of ball-shaped lung growths formed by computed tomography are examined; the images of the ball-shaped lung growths (BLG) are presented by a set of sections formed at the single stage. The given set of sections is used to determine the stochastic parameters of a density value of the ball-shaped lung growth average for the set of sections, a correlation coefficient determined by describing as a parabolic relation of the density value of the ball-shaped lung growth average for the section and a number of the section, a root mean square deviation of the density value of the ball-shaped lung growth average for the set of sections, a correlation coefficient determined by describing as a parabolic relation of the root mean square deviation of the density value of the ball-shaped lung growth average for the set of sections and a number of the section, and the fractal parameters of a fractal dimension of the internal structure of the ball-shaped lung growth average for the set of sections, and the root mean square deviation of the fractal dimension. The stochastic and fractal parameters are analysed by using an artificial neuron, and an activation function is evaluated. The activation function of more than 0.5 enables classifying the ball-shaped lung growth as cancer, while the activation function of less than 0.5 shows the ball-shaped lung growth classified as infiltrative tuberculosis.
EFFECT: technique enables providing the more reliable differential diagnosis of the ball-shaped lung growth when examining tumour and inflammatory processes, and reducing a radiation dose absorbed by the patient.
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Authors
Dates
2014-11-20—Published
2013-05-13—Filed