title: ÐÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² ÑÐ°Ð´Ð¸Ð¾Ð¼Ð¸ÐºÐ¸ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÐÐ¢ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Features of application of radiomics features for CT image classification
Creators: name: Ð¢Ð°ÑÐ°Ð½Ð¾Ð²Ð°, ÐÐ°ÑÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÑÐ°Ðº Ð»ÐµÐ³ÐºÐ¸Ñ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð°Ñ ÑÐ¾Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ñ
ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÑÐ°Ð´Ð¸Ð¾Ð¼Ð¸ÐºÐ°
Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð¸Ðµ Ð¸ Ð²ÑÐ±Ð¾Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð²
Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
ÑÑÐ°Ð´Ð¸Ð¸ Ð¢
lung cancer
computed tomography
image filtering
radiomics
feature extraction and selection
neural network
classification
stages T

description: ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â ÐÐ¢ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ ÑÐ°ÐºÐ° Ð»ÐµÐ³ÐºÐ¾Ð³Ð¾. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â ÑÐ¿ÑÐ¾ÑÐµÐ½Ð¸Ðµ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ ÑÐ°ÐºÐ° Ð»ÐµÐ³ÐºÐ¸Ñ, Ð´Ð»Ñ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ ÐµÐµ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸, Ð¿ÑÑÐµÐ¼ Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ñ ÐÐ¢ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ°Ð´Ð¸Ð¾Ð¼Ð¸ÐºÐ¸ Ð¸ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸ÐµÐ¹ Ð¿Ð¾ ÑÑÐ°Ð´Ð¸ÑÐ¼ Ð¢, Ð¿ÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ 95 ÑÐ½Ð¸Ð¼ÐºÐ¾Ð² ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð¹ ÑÐ¾Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ð¸ Ñ ÑÐ°ÐºÐ¾Ð¼ Ð»ÐµÐ³ÐºÐ¸Ñ Ð±ÑÐ»Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ñ Ð½Ð° Ð´Ð²Ðµ Ð³ÑÑÐ¿Ð¿Ñ Ð¿Ð¾ ÑÑÐ°Ð´Ð¸ÑÐ¼ Ð¢: Ð¢1 Ð¸ Ð¢2. ÐÐµÑÐ¾Ð´ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ ÑÑÐ°Ð´Ð¸Ð¸ ÑÐ°ÐºÐ° Ð»ÐµÐ³ÐºÐ¸Ñ Ð½Ð° ÐÐ¢ ÑÐ½Ð¸Ð¼ÐºÐ°Ñ, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¾Ð¿Ð¸ÑÐ°Ð½ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ, ÑÐ¾ÑÑÐ¾Ð¸Ñ Ð¸Ð· ÑÐ»ÐµÐ´ÑÑÑÐ¸Ñ ÑÑÐ°Ð¿Ð¾Ð²: â¢ ÐÑÐ¸Ð¾Ð±ÑÐµÑÐµÐ½Ð¸Ðµ Ð±Ð°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð¹ ÑÐ¾Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ð¸; â¢ ÐÑÐµÐ´Ð²Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð°Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´Ð°Ð½Ð½ÑÑ; â¢ ÐÐ·Ð²Ð»ÐµÑÐµÐ½Ð¸Ðµ Ð¸ Ð²ÑÐ±Ð¾Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð²; â¢ ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ¾ÑÐ»Ðµ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð· 95 ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½ÑÐµ ÑÐ¾Ð¼Ð¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ¾Ð² Ñ ÑÐ°ÐºÐ¾Ð¼ Ð»ÐµÐ³ÐºÐ¸Ñ, 50 Ð¸Ð¼ÐµÐ»Ð¸ ÑÑÐ°Ð´Ð¸Ñ T1, Ð° 45 - ÑÑÐ°Ð´Ð¸Ñ T2. ÐÑÐ¸ Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²ÐºÐµ Ðº ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð¾ÑÐºÑÑÑÑÐµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÑÑÑÑ Ð¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸. ÐÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÑÑÐµÐ´ÑÑÐ²Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ (Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹) ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ¾Ðº. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº: Matlab, Python.
description: The object of the study is CT images of lung cancer. The purpose of the work is to simplify the diagnosis of lung cancer, to increase its accuracy, by extracting features from CT images using radiomics and further classification by T stages, using a trained neural network. The 95 CT images with lung cancer were classified into two groups by T stage: T1 and T2. The method for determining the stage of lung cancer on CT scans, which is described in this paper, consists of the following steps: â¢ Acquisition of a computed tomography database; â¢ Pre-processing the data; â¢ Extraction and selection of features; â¢ Data classification. After classification, of the 95 CT scans of patients with lung cancer, 50 had stage T1 and 45 had stage T2. Open educational resources and information retrieval and analysis software were used in preparation for the work. Automated (automated) development tools were not used. Used software such as: Matlab, Python. â