Detection of fibrosis regions in the lungs based on CT scans / Natzina Juanita Francis ; sci. adv. S. V. Aksenov
Уровень набора: (RuTPU)RU\TPU\conf\25267, Информационные технологии в науке, управлении, социальной сфере и медицине, сборник научных трудов IV Международной научной конференции, 5-8 декабря 2017 г., Томск, в 2 ч. / Национальный исследовательский Томский политехнический университет (ТПУ) = 2017Язык: английский ; резюме, eng.Страна: Россия.Резюме или реферат: The main aim in the article was to provide an accurate, simple and fast algorithm that can increase the performance of the system and thereby the efficiency. Accurate results for lung images have not been accurate as the edges form in many diverse ways. Thereby, a universally applicable edge detection algorithm cannot comply with the purpose of detecting fibrosis. Thus by considering and furthermore introducing a deep convolutional neural network with pixel manipulation, the detection of fibrosis can be made easy, efficient and even accurate unlike the traditional learning structures. By implementing this we are free from extraction of features or even computation of multiple channels and thus suggesting a very straight forward method in terms of the detection and output accuracy..Примечания о наличии в документе библиографии/указателя: [Библиогр.: с. 8-9 (8 назв.)].Тематика: электронные ресурсы | труды учёных ТПУ | images | neural network | томографические изображения | нейронные сети | методы обработки | фиброз | легкие Ресурсы он-лайн:Щелкните здесь для доступа в онлайнЗаглавие с титульного экрана
[Библиогр.: с. 8-9 (8 назв.)]
The main aim in the article was to provide an accurate, simple and fast algorithm that can increase the performance of the system and thereby the efficiency. Accurate results for lung images have not been accurate as the edges form in many diverse ways. Thereby, a universally applicable edge detection algorithm cannot comply with the purpose of detecting fibrosis. Thus by considering and furthermore introducing a deep convolutional neural network with pixel manipulation, the detection of fibrosis can be made easy, efficient and even accurate unlike the traditional learning structures. By implementing this we are free from extraction of features or even computation of multiple channels and thus suggesting a very straight forward method in terms of the detection and output accuracy.
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