title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹ Ð»ÐµÐ³ÐºÐ¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð³ÑÐ°ÑÐ¸Ð¸ Ð³ÑÑÐ´Ð½Ð¾Ð¹ ÐºÐ»ÐµÑÐºÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of a software system for recognizing lung diseases based on chest radiography
Creators: name: ÐÑÐ¼Ð°ÐµÐ², Ð 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: 2023
Subjects: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ðµ Ð»ÐµÐ³ÐºÐ¸Ñ
ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð³ÑÐ°Ð¼Ð¼Ð° Ð³ÑÑÐ´Ð½Ð¾Ð¹ ÐºÐ»ÐµÑÐºÐ¸
convolutional neural network
deep learning
lung disease
chest x-ray

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description: The masterâs thesis is devoted to the development of a software system and improving the quality of recognition of lung diseases based on chest X-ray. A review of the existing methods and approaches for diagnosing lung diseases by chest radiographs and their features was carried out. An overview of the most relevant open access chest radiography datasets, methods and features of data augmentation, image preprocessing was also presented. As part of this work, based on obtained results, a method was proposed for automatic classification and prediction of pulmonary pathologies in frontal chest radiography. The presented approach is based not only on the recognition of diseases by the image of the chest, but also on the use of image metadata (age, gender of the patient, type of image acquisition). Several architectures were considered, various depths of models based on deep neural networks for extracting features from an image, methods for preprocessing images were proposed and implemented to highlight a key region of the lungs in the image. The effectiveness and quality of recognition of the proposed approach for various architectures was compared with other pathology classification methods on a publicly available test set of a dataset containing more than 112 thousand labeled lung images. As a result of this work, a working system for recognizing lung diseases based on chest x-ray and patient metadata was developed and obtained using trained models based on deep neural networks.