Laryngeal Image

Laryngeal image analysis focuses on extracting clinically relevant information from images of the larynx, primarily for early cancer detection and improved surgical procedures. Current research employs deep learning models, including Convolutional Neural Networks (CNNs) and Transformers, to classify laryngeal lesions, predict patient demographics (age, gender, smoking history) to enhance diagnostic accuracy, and guide minimally invasive procedures like robotic tracheotomies. These advancements leverage techniques like transfer learning and novel kernel fusion methods to improve model performance and efficiency, ultimately aiming to improve patient outcomes and streamline clinical workflows.