Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …

Abstract Machine learning models have been increasingly considered to model head and
neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of …

Background The objective of this study was to assess the performance and application of a
self‐developed deep learning (DL) algorithm for the real‐time localization and classification …

Objective The endoscopic and laryngoscopic examination is paramount for laryngeal,
oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer …

Background Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal
cancer, whose early diagnosis can significantly improve the prognosis and quality of life of …

Artificial intelligence is being increasingly seen as a useful tool in medicine. Specifically,
these technologies have the objective to extract insights from complex datasets that cannot …

Laryngeal disease classification is a relatively hard task in medical image processing
resulting from its complex structures and varying viewpoints in data collection. Some existing …

Laryngeal cancer detection on throat area images is a vital application of medical image
diagnosis and computer vision (CV) in the healthcare domain. It contains the analysis and …

Objective To summarize the use of deep learning in the detection of voice disorders using
acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy …

Nasal endoscopy is routinely performed to distinguish the pathological types of masses.
There is a lack of studies on deep learning algorithms for discriminating a wide range of …