Trans RCED-UNet3+: a hybrid CNN-transformer model for precise lung nodule segmentation
These findings underscore the value of merging convolutional and transformer architectures, delivering a robust approach for precise segmentation in medical imaging. This model enhances the detection of subtle and irregular structures, enabling more accurate lung cancer diagnoses in clinical environments.
Authors
- Nouf Abdullah Almujally (ORCID: https://orcid.org/0000-0003-4405-9912)
- Nada Alasbali (ORCID: https://orcid.org/0000-0003-2541-7876)
- Razia Zia (ORCID: https://orcid.org/0000-0002-1654-5334)
- Irfan Ahmed Usmani (ORCID: https://orcid.org/0000-0002-8518-2899)
- Muhammad Fainan Hanif (ORCID: https://orcid.org/0000-0002-0273-8258)
- Sadaf Raza
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Iqra University (PK)
- Örebro University (SE)
- Sir Syed University of Engineering and Technology (PK)
- Habib University (PK)
- King Khalid University (SA)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-16
- DOI
- https://doi.org/10.3389/fonc.2025.1654466
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Princess Nourah Bint Abdulrahman University
- King Khalid University