Identification of bronchopulmonary segment containing COVID abrasions using EG-CNN and Segnet / S. V. Aksenov, N. S. Samuel Ragland Francis, N. J. Samuel Ragland Francis
Язык: английский.Страна: Россия.Серия: Искусственный интеллект и машинное обучениеРезюме или реферат: As the current COVID pandemic is a huge concern, more effective methods are required for treatment and analysis of this disease. If COVID analysis is aided by automated detection of the disease, this will reduce time and also speed up treatment. In this research, the particular bronchopulmonary segment containing COVID is detected to narrow and segregate the treatment area. Computer Tomographic Images are passed through EG-CNN which is modelled with Segnet to detect COVID-19 abrasions. The output of the two CNNs are gated to develop the final result with high accuracy..Примечания о наличии в документе библиографии/указателя: [Библиогр.: с. 98 (16 назв.)].Тематика: электронный ресурс | труды учёных ТПУ | идентификация | томографические изображения | компьютерные изображения | COVID-19 | сегментация | диагностика Ресурсы он-лайн:Щелкните здесь для доступа в онлайнНет реальных экземпляров для этой записи
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[Библиогр.: с. 98 (16 назв.)]
As the current COVID pandemic is a huge concern, more effective methods are required for treatment and analysis of this disease. If COVID analysis is aided by automated detection of the disease, this will reduce time and also speed up treatment. In this research, the particular bronchopulmonary segment containing COVID is detected to narrow and segregate the treatment area. Computer Tomographic Images are passed through EG-CNN which is modelled with Segnet to detect COVID-19 abrasions. The output of the two CNNs are gated to develop the final result with high accuracy.
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