Multiclassification Of Vocal Folds Disorders From Videos By Spatio-Temporal Deep Features

In this paper, we are interested in detecting and classifying vocal folds disorders from high speed videos. Our contribution aims to jointly exploit the spatial and temporal information related to the vocal folds, by resorting to various 3 D spatiotemporal architectures. Another novelty consists in evaluating the influence of the region of interest delineation on the multiclassification performances. In our experiments, several types of disorders have been considered on high speed endoscopy videos. Experimental results indicate the gain achieved by capturing the spatio-temporal features.