Comparative study of COVID and pulmonary fibrotic CT lung images using siamese networks with VGG16; Молодежь и современные информационные технологии
| Parent link: | Молодежь и современные информационные технологии.— 2021.— [С. 99-101] |
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| Huvudupphovsman: | |
| Institutionell upphovsman: | |
| Övriga upphovsmän: | , |
| Sammanfattning: | Заглавие с титульного экрана In this research an algorithm is proposed to produce comparative results between Pulmonary Fibrosis of the lungs and COVID computer tomography lung images for the purpose of research to aid in the field of medical science. The Siamese Network which is based on parallel tandem operation to produce comparative results, is altered by changing or altering the implementation function using the VGG16 neural network. The input data set in the method uses a variation of healthy lung CT images along with CT images of cases with pulmonary fibrosis and COVID. The main aim is to produce a comparative study on the textural variation of the CT images under study to further enhance research outputs in the future with accuracy and less time consumption. |
| Språk: | engelska |
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2021
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| Serie: | Искусственный интеллект и машинное обучение |
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| Länkar: | http://earchive.tpu.ru/handle/11683/68022 |
| Materialtyp: | Elektronisk Bokavsnitt |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=633162 |
| Sammanfattning: | Заглавие с титульного экрана In this research an algorithm is proposed to produce comparative results between Pulmonary Fibrosis of the lungs and COVID computer tomography lung images for the purpose of research to aid in the field of medical science. The Siamese Network which is based on parallel tandem operation to produce comparative results, is altered by changing or altering the implementation function using the VGG16 neural network. The input data set in the method uses a variation of healthy lung CT images along with CT images of cases with pulmonary fibrosis and COVID. The main aim is to produce a comparative study on the textural variation of the CT images under study to further enhance research outputs in the future with accuracy and less time consumption. |
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