Improving real-time detection of laryngeal lesions in endoscopic images using a decoupled super-resolution enhanced YOLO

• Published in: Computer methods and programs in biomedicine Vol. 260; p. 108539
• Main Authors: Baldini, Chiara, Migliorelli, Lucia, Berardini, Daniele, Azam, Muhammad Adeel, Sampieri, Claudio, Ioppi, Alessandro, Srivastava, Rakesh, Peretti, Giorgio, Mattos, Leonardo S.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.03.2025
• Subjects: Algorithms; Decision support system; Deep Learning; Endoscopy; Humans; Image Processing, Computer-Assisted - methods; Laryngeal lesions; Laryngeal Neoplasms - diagnostic imaging; Laryngoscopy; Larynx - diagnostic imaging; Larynx - pathology; Narrow Band Imaging; Real-time assistance; Super resolution; Deep learning; Endoscopy; Decision support system; Laryngeal lesions; Super resolution; Real-time assistance
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2024.108539

Laryngeal Cancer (LC) constitutes approximately one third of head and neck cancers. Detecting early-stage lesions in this anatomical region is crucial for achieving a high survival rate. However, it poses significant diagnostic challenges owing to the varied appearance of lesions and the need for pr...