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R-FCN Based Laryngeal Lesion Detection

In recent years, with the emergence of high-computing GPUs and the introduction of new convolutional neural network models, deep learning technology has ushered in a new round of development. At the same time, in the era of rapid development of the Internet, a large amount of high-quality medical image data is generated every day, which provides a new opportunity for the deep learning to develop in the field of automatic analysis and auxiliary diagnosis of medical images. In this paper, R-FCN (region-based fully convolutional networks) are introduced into the lesion detection of the larynx, and the classification result of the lesion can be given while confirming the lesion area. In view of the existing image-based classification, there can only be one organ in the image and the background must be simple. Through the target detection method, multi-organ target can be realized and detected under complex background. The experimental results show that the target in complex scenes can be effectively identified when the training data is sufficient.