Статья

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

N. Francis, N. Francis, S. Axyonov, S. Aljasar, Y. Xu, M. Saqib,
2020

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 multi-scale CNN, and localizes areas of cystic fibrosis in the lungs with high accuracy.

Цитирование

Похожие публикации

Документы

Версии

  • 1. Version of Record от 2020-08-26

Метаданные

Об авторах
  • N. Francis
    Engineering School of Information Technology and Robotics, Tomsk Polytechnic University, Lenin Avenue, 30, Tomsk, 634034, Russia
  • N. Francis
    Engineering School of Information Technology and Robotics, Tomsk Polytechnic University, Lenin Avenue, 30, Tomsk, 634034, Russia
  • S. Axyonov
    Engineering School of Information Technology and Robotics, Tomsk Polytechnic University, Lenin Avenue, 30, Tomsk, 634034, Russia
  • S. Aljasar
    School of Nuclear Science and Engineering, Tomsk Polytechnic University, Lenin Avenue, 30, Tomsk, 634034, Russia
  • Y. Xu
    School of Nuclear Science and Engineering, Tomsk Polytechnic University, Lenin Avenue, 30, Tomsk, 634034, Russia
  • M. Saqib
    Research School of Chemistry and Applied Biomedical Sciences, Tomsk Polytechnic University, Lenin Avenue, 30, Tomsk, 634034, Russia
Название журнала
  • Journal of Physics Conference Series
Том
  • 1611
Выпуск
  • 1
Страницы
  • 012062
Издатель
  • IOP Publishing
Тип документа
  • journal article
Тип лицензии Creative Commons
  • CC BY
Правовой статус документа
  • Свободная лицензия
Источник
  • dimensions