Combined Application of Deep Learning and Radiomic Features for Classification of Lung CT Images
Introduction. In oncology, accurate classification of lung cancer mutations plays a key role in developing personalized treatment strategies. Lung cancer, distinguished by its heterogeneity, presents significant challenges in diagnosis and treatment, requiring innovative approaches for precise mutat...
Saved in:
| Main Authors: | Shariati Faridoddin, V. A. Pavlov |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Saint Petersburg Electrotechnical University "LETI"
2025-03-01
|
| Series: | Известия высших учебных заведений России: Радиоэлектроника |
| Subjects: | |
| Online Access: | https://re.eltech.ru/jour/article/view/975 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting Invasiveness in Lepidic Pattern Adenocarcinoma of Lung: Analysis of Visual Semantic and Radiomic Features
by: Sean F. Johnson, et al.
Published: (2024-10-01) -
Computed tomography radiomics of intratumoral and peritumoral microenvironments for identifying the invasiveness of subcentimeter lung adenocarcinomas
by: Yu-Qiang Zuo, et al.
Published: (2025-08-01) -
Spatial encoding and growth-related change of sheep lung radiomic features
by: David Collie, et al.
Published: (2025-02-01) -
Diagnostic value of CT radiomics and clinical features in differentiating focal organizing pneumonia from peripheral lung cancer
by: Weihua Tang, et al.
Published: (2025-06-01) -
Predicting the invasiveness of pulmonary adenocarcinoma using intratumoral and peritumoral radiomics features
by: Jingjing Hong, et al.
Published: (2025-05-01)