Identification of bronchopulmonary segment containing COVID abrasions using EG-CNN and Segnet; Молодежь и современные информационные технологии
| Parent link: | Молодежь и современные информационные технологии.— 2021.— [С. 96-98] |
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| Muut tekijät: | , |
| Yhteenveto: | 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. |
| Kieli: | englanti |
| Julkaistu: |
2021
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| Sarja: | Искусственный интеллект и машинное обучение |
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| Linkit: | http://earchive.tpu.ru/handle/11683/68021 |
| Aineistotyyppi: | Elektroninen Kirjan osa |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=633161 |
| Yhteenveto: | 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. |
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