Advancing chest X-ray diagnostics: A novel CycleGAN-based preprocessing approach for enhanced lung disease classification in ChestX-Ray14
This research highlights the importance of considering the presence of artifacts when diagnosing lung diseases from radiographic images. By eliminating unwanted noise, our approach enables models to focus on relevant diagnostic features, thereby improving their performance. The results demonstrated that our approach is promising, highlighting its potential for broader applications in lung disease classification.
Authors
- Pierre Chauvet (ORCID: https://orcid.org/0000-0002-3843-5179)
- Nassib Abdallah (ORCID: https://orcid.org/0000-0002-0488-585X)
- Mohamad Oueidat
- Mathieu Hatt (ORCID: https://orcid.org/0000-0002-8938-8667)
- Aya Hage Chehade (ORCID: https://orcid.org/0000-0001-9642-565X)
- Jean-Marie Marion (ORCID: https://orcid.org/0000-0002-3656-5914)
Institutions
- Inserm (FR)
- Lebanese University (LB)
- Université de Bretagne Occidentale (FR)
- Laboratoire Angevin de Recherche en Mathématiques (FR)
- Laboratoire de Traitement de l'Information Médicale (FR)
- Université d'Angers (FR)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-11-25
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108518
- Citations
- 22
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 8.89