Identification of bronchopulmonary segment containing COVID abrasions using EG-CNN and Segnet
| Parent link: | Молодежь и современные информационные технологии: сборник трудов XVIII Международной научно-практической конференции студентов, аспирантов и молодых учёных, 22-26 марта 2021 г., г. Томск/ Национальный исследовательский Томский политехнический университет, Инженерная школа информационных технологий и робототехники ; под ред. Н. Г. Маркова [и др.]. [С. 96-98].— , 2021 |
|---|---|
| Main Author: | |
| Corporate Author: | |
| Other Authors: | , |
| Summary: | Заглавие с титульного экрана 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. |
| Published: |
2021
|
| Series: | Искусственный интеллект и машинное обучение |
| Subjects: | |
| Online Access: | http://earchive.tpu.ru/handle/11683/68021 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=633161 |