Diagnostic of Cystic Fibrosis in Lung Computer Tomographic Images using Image Annotation and Improved PSPNet Modelling

Diagnostic of Cystic Fibrosis in Lung Computer Tomographic Images using Image Annotation and Improved PSPNet Modelling

Bibliographic Details
Parent link:Journal of Physics: Conference Series
Vol. 1611 : Prospects of Fundamental Sciences Development (PFSD-2020).— 2020.— [012062, 6 p.]
Corporate Author: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Other Authors: Samuel Ragland Francis N. J. Natzina Juanita, Samuel Ragland Francis N. S. Nadine Susanne, Aksenov S. V. Sergey Vladimirovich, Aljasar S. A., Xu Y., Saqib M. Muhammad
Summary:Title screen
The research deals with the development of an algorithm for detecting pathological formation in cystic fibrosis using the PSPNet model with focal loss. The model allows data sets to be entered in accordance to their similarities based on their pathological diagnostic signs. The simple and effective algorithm structure groups annotated images, processes them in a multiscale CNN, and localizes areas of cystic fibrosis in the lungs with high accuracy.
Published: 2020
Subjects:
электронный ресурс
труды учёных ТПУ
диагностика
компьютерные томографы
томографические изображения
легкие
патология
изображения
легочные поражения
Online Access:https://doi.org/10.1088/1742-6596/1611/1/012062
http://earchive.tpu.ru/handle/11683/63235
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662793

Internet

https://doi.org/10.1088/1742-6596/1611/1/012062
http://earchive.tpu.ru/handle/11683/63235

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