УДК: 004.932.2

Comparative analysis of methods for classifying pulmonary nodules from computer-tomography images

Glaznev, M.Y., Gusarova, N.F., Kotsyuba, I.Y., Ryabchikov, I.A., Sergeeva, M.V.

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Глазнев М.Ю., Гусарова Н.Ф., Коцюба И.Ю., Рябчиков И.А., Сергеева М.В. Сравнительный анализ методов классификации легочных узлов по изображениям компьютерной томографии // Оптический журнал. 2017. Т. 84. № 1. С. 58–68.

Glaznev M.Yu., Gusarova N.F., Kotsyuba I.Yu., Ryabchikov I.A., Sergeeva M.V. Comparative analysis of methods for classifying pulmonary nodules from computer-tomography images [in Russian] // Opticheskii Zhurnal. 2017. V. 84. № 1. P. 58–68.

For citation (Journal of Optical Technology):

M. Yu. Glaznev, N. F. Gusarova, I. Yu. Kotsyuba, I. A. Ryabchikov, and M. V. Sergeeva, "Comparative analysis of methods for classifying pulmonary nodules from computer-tomography images," Journal of Optical Technology. 84(1), 41-48 (2017). https://doi.org/10.1364/JOT.84.000041

Abstract:

This paper gives a comparative analysis of methods for classifying pulmonary nodules using computer-tomography images and a comparative evaluation of the information content of the attributes that are used, as well as an estimate of the effectiveness of classifying pulmonary nodules using various machine-learning algorithms. Problems involving the visual classification of pulmonary nodules and conditions for improving its accuracy are investigated. Sets of the most informative attributes are chosen for classifying pulmonary nodules. The accuracies with which pulmonary nodules are classified into benign and malignant are obtained.

Keywords:

pulmonary nodules, computer-tomography images, machine learning

OCIS codes: 100.6950

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