title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿Ð¾Ð´ÑÐ¸ÑÑÐµÐ¼Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ 3D Ð½Ð¾Ð²Ð¾Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð² Ð»ÐµÐ³ÐºÐ¾Ð¼ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ¸Ð°Ð¼ÑÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
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: Development of a subsystem for classifying segmented 3D neoplasms in the lung using siamese neural networks
Creators: name: Ð­Ð´Ð¾Ð¼ÑÐºÐ¸Ð¹, ÐÐ»ÐµÐºÑÐ°Ð½Ð´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: 2020
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ¸Ð°Ð¼ÑÐºÐ°Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ¼Ð±ÐµÐ´Ð´Ð¸Ð½Ð³
Ð¼ÐµÑÐ¾Ð´ k Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹
Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ° ÑÐ°ÐºÐ° Ð»ÐµÐ³ÐºÐ¾Ð³Ð¾
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð¹ ÑÐ¾Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ð¸
machine learning
neural network
convolutional neural network
siamse convolutional neural network
embedding
k-nn
diagnostics of lung cancer
processing images of computer tomography

description: Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¿ÑÐµÐ´Ð½Ð°Ð·Ð½Ð°ÑÐµÐ½Ð½ÑÐ¹ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ°ÐºÐ¾Ð²ÑÑ Ð¾Ð¿ÑÑÐ¾Ð»ÐµÐ¹. Ð Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð»ÐµÐ¶Ð¸Ñ ÐºÐ¾Ð½ÑÐµÐ¿ÑÐ¸Ñ ÑÐ¸Ð°Ð¼ÑÐºÐ¸Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ (SCNN). SCNN Ð¾Ð±ÑÑÐ°ÐµÑÑÑ Ð½Ð° ÑÐ½Ð¸Ð¼ÐºÐ°Ñ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð¹ ÑÐ¾Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ð¸ Ð»ÐµÐ³ÐºÐ¾Ð³Ð¾, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ Ð² Ð²Ð¸Ð´Ðµ ÑÑÐµÑÐ¼ÐµÑÐ½ÑÑ Ð¼Ð°ÑÑÐ¸Ð²Ð¾Ð² Ð´Ð°Ð½Ð½ÑÑ. SCNN Ð¿ÐµÑÐµÐ²Ð¾Ð´Ð¸Ñ Ð¸ÑÑÐ¾Ð´Ð½ÑÐµ ÑÐ½Ð¸Ð¼ÐºÐ¸ Ð² ÑÐ¼Ð±ÐµÐ´Ð´Ð¸Ð½Ð³-Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²Ð¾, Ð¾ÑÐ´Ð°Ð»ÑÑ Ð¾Ð±ÑÐµÐºÑÑ ÑÐ°Ð·Ð½Ð¾Ð³Ð¾ ÐºÐ»Ð°ÑÑÐ° Ð¸ ÑÐ±Ð»Ð¸Ð¶Ð°Ñ Ð¾Ð±ÑÐµÐºÑÑ Ð¾Ð´Ð½Ð¾Ð³Ð¾ ÐºÐ»Ð°ÑÑÐ°, Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ ÑÑÐ½ÐºÑÐ¸Ð¸ Ð¿Ð¾ÑÐµÑÑ triplet loss. ÐÐ¾ÑÐ»Ðµ Ð¿ÐµÑÐµÐ²Ð¾Ð´Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð² ÑÐ¼Ð±ÐµÐ´Ð´Ð¸Ð½Ð³-Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²Ð¾, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐµ Ð²ÐµÐºÑÐ¾ÑÐ° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÑÐ¸ÑÑÑÑÑÑ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð¸Ð· Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÑÑÑÑ ÑÐ°ÐºÐ¸Ðµ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÐºÐ°Ðº: ÑÐ°Ð·Ð¼ÐµÑ Ð²ÑÐ¾Ð´Ð½Ð¾Ð³Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ, ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÑ ÑÐ¼Ð±ÐµÐ´Ð´Ð¸Ð½Ð³-Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²Ð°, ÑÐ¿Ð¾ÑÐ¾Ð±Ñ Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ Ð´ÑÑÐ³Ð¸Ðµ. ÐÐ°Ð¸Ð»ÑÑÑÐ°Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð°Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ â 0.92.
description: The paper considers a machine learning algorithm designed to solve the classification of cancerous tumors. The algorithm is based on the concept of Siamese convolutional neural networks (SCNN). SCNN is trained on computed tomography images of the lung, which are presented in the form of three-dimensional data arrays. SCNN translates the original images into the embedding space, moving objects of different classes and bringing objects of the same class closer using the triplet loss function. After translating the images into the embedding space, the resulting vectors are classified using one of the machine learning algorithms. The work compares such hyperparameters of the learning algorithm as: the size of the input image, the dimension of the embedding space, methods of pre-training the model, and others. The best classification accuracy obtained using the model is 0.92.