关键词: 深度学习, 神经网络, 特征, 喉镜, 咽喉新生物

Abstract: Throat new organism is the disease that new organisms grow in the area of vocal cord, which is one of common diseases that affect normal function of throat. At present, the throat new tissues are mainly diagnosed by doctors using laryngoscope photos. However, misdiagnosed cases happened because of the differences of individual clinical experience of doctors in the diagnosis of new tissue diseases often have different diagnostic results. And new tissue diseases if not timely diagnosed and treated is possible to evolve into laryngeal cancer. Based on this, this paper proposes a learning algorithm of throat new tissue disease based on deep learning. Through the multi-layer convolution kernels extracted features from a large number of labeled training sets, and only the effective features are retained in the process of reverse propagation. The studied model has an impressive fitting performance on training set and also a good generalization performance on test set. It has a certain value in the practical application.

Key words: deep learning, neural network, features, laryngoscope, throat new organisms