A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
This study highlights the benefits of combining GANs, LSTM, and VGG16 into a unified framework. This approach significantly improves the accuracy of detecting and reconstructing lung tumors, promising to enhance diagnostic methods and patient results in lung cancer treatment.
Authors
- Siamak Pedrammehr (ORCID: https://orcid.org/0000-0002-2974-1801)
- Seyed Vahid Moravvej
- Marzieh Mirzaeibonehkhater
- Hamidreza Najafi (ORCID: https://orcid.org/0009-0005-8887-0389)
- Roohallah Alizadehsani (ORCID: https://orcid.org/0000-0003-0898-5054)
- Kimia Savoji (ORCID: https://orcid.org/0009-0008-8434-8755)
Institutions
- Deakin University (AU)
- Isfahan University of Technology (IR)
- Indiana University – Purdue University Indianapolis (US)
- Tabriz Islamic Arts University (IR)
- Iran University of Science and Technology (IR)
- Clemson University (US)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3390/diagnostics14222604
- Citations
- 19
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 7.96