Improving real-time detection of laryngeal lesions in endoscopic images using a decoupled super-resolution enhanced YOLO
This research underscores the potential of SRE-YOLO in developing efficient DL-driven decision support systems for real-time detection of laryngeal lesions at different scales from both WL and NBI endoscopic data.
Authors
- Claudio Sampieri (ORCID: https://orcid.org/0000-0002-7699-2291)
- Muhammad Adeel Azam (ORCID: https://orcid.org/0000-0001-9226-1838)
- Daniele Berardini (ORCID: https://orcid.org/0000-0001-7009-6317)
- Alessandro Ioppi (ORCID: https://orcid.org/0000-0002-2764-715X)
- Giorgio Peretti (ORCID: https://orcid.org/0000-0003-2362-8936)
- Leonardo S. Mattos (ORCID: https://orcid.org/0000-0002-8511-9144)
- Lucia Migliorelli (ORCID: https://orcid.org/0000-0003-2388-1501)
- Rakesh Srivastava (ORCID: https://orcid.org/0000-0002-5345-532X)
- Chiara Baldini (ORCID: https://orcid.org/0009-0006-5287-2478)
Institutions
- Marche Polytechnic University (IT)
- Orthopedic Specialty Hospital (US)
- Italian Institute of Technology (IT)
- Provincia Autonoma di Trento (IT)
- Ospedale Policlinico San Martino (IT)
- University of Genoa (IT)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108539
- Citations
- 15
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.28