title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð¹ Ð¿Ð¾Ð´ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð½Ð¾Ð²Ð¾Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð² Ð»ÐµÐ³ÐºÐ¾Ð¼
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 an intellectual subsystem of pulmonary nodule detection
Creators: name: Ð¢ÑÐ»ÑÐ¿Ð¸Ð½, ÐÐ¸ÐºÑÐ¾Ñ
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: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
cÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð´ÐµÑÐµÐºÑÐ¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¸
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð°Ñ ÑÐ¾Â­Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ñ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¾Ð¿ÑÑ Ð¾Ð»ÐµÐ¹ Ð² Ð»ÐµÐ³ÐºÐ¸Ñ
deep learning
convolutional neural networks
object detection on images
computer tomography
pulmonary nodules detection

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description: The subject of the graduate qualification work is "Development of an intellectual subsystem of pulmonary nodules detection". The diploma thesis deals with the development of neural network architecture to solve the problem of computer vision on the images of computer tomography: the detection of nodules in the lung. The following tasks were solved in the course of the work: â Research of automatic computer diagnostics methods, â Research of application of neural networks to the task of computer vision - object detection, â Preparation of data sets consisting of multiple CT images for further training and testing of developed models, â Development of a neural network architecture to detect pulmonary nodules in CT images, â Testing of the developed neural network architecture on computer tomography images. As a result of the work the program module in Python language was written using PyTorch deep learning library, which trains neural network of the developed architecture. For conducting experiments I used publicly available LIDC-IDRI datase, for testing the trained networks the LUNA16 competition script. As a result the average value of Sensitivity metric is 0.978. The paper concludes with a presentation of findings and a description of possible ways to improve the results obtained.