Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network
The DDDG-GAN model mitigates mode collapse and improves generalizability in lung nodule classification. It demonstrates superior performance on both the LIDC-IDRI and the unseen LUNA16 and LungX datasets, offering significant potential for improving diagnostic accuracy in clinical practice.
Authors
- Ahmed Saihood (ORCID: https://orcid.org/0000-0002-0147-7121)
- Mohamed Manoufali (ORCID: https://orcid.org/0000-0002-9140-6529)
- Wijdan Rashid Abdulhussien
- Laith Alzubaid
- Yuantong Gu (ORCID: https://orcid.org/0000-0002-2770-5014)
Institutions
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Translational Research Institute (AU)
- Queensland University of Technology (AU)
- Thi Qar University (IQ)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1186/s12911-024-02820-9
- Citations
- 7
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.93
Funders
- Queensland University of Technology
- Australian Research Council