Classification of vocal fold disorders in high speed videos by deep learning

In this paper, we are interested in the design of deep-learning-based computer-aided diagnosis systems that recognize laryngeal diseases. Our contribution consists in studying the impact of exploiting the spatio-temporal information on the classification performance. Experimental results, carried out on vocal fold high speed videos allow to compare the performances of the spatio-temporal deep neural network network with those of a purely spatial deep architecture.