Segmentation of honeycomb cysts, traction bronchiectasis and emphysematous lung parenchyma using the deep learning method
In this study, we successfully utilized the U-Net architecture, a deep learning technology, to accurately segment honeycomb cysts in MinIP images, one of the parameters that will help classify interstitial lung diseases (ILDs). We anticipate that our study will guide future studies on the classification of ILDs.
Authors
- Alper Odabaş (ORCID: https://orcid.org/0000-0002-4361-3056)
- Ahmet Faruk Aslan (ORCID: https://orcid.org/0000-0003-1583-6508)
- Füsun Alataş (ORCID: https://orcid.org/0000-0002-9321-7721)
- Özer Çelik (ORCID: https://orcid.org/0000-0002-4409-3101)
- Serhat DEMİR
- Hüseyin YILDIRIM
- Büşra SARIAY
- Burcu MERT
- Nevin AYDIN
Institutions
- State Hospital (GB)
- Eskişehir City Hospital (TR)
- Eskişehir Osmangazi University (TR)
Publication Details
- Journal
- Tuberkuloz ve Toraks
- Published
- 2025-12-23
- DOI
- https://doi.org/10.5578/tt.2025041118
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00