Laryngeal Tumor Detection in Endoscopic Images Based on Convolutional Neural Network

Laryngeal tumor is a typical head and neck disease that may be cancerous, causing harm to human health. Automatic laryngeal tumor detection in laryngeal endoscopic images is beneficial to the further analysis of tumor characteristics to aid in treatment, such as computer assisted surgery. However, there have been very few attempts to automatically detect the tumor of larynx. In this paper, three commonly used object detection models based on convolutional neural networks (CNNs) are used to achieve automatic detection of the tumors on a dataset of laryngeal endoscopic images. As far as we know, this is the first time that object detection models based on CNNs have been used to create end-to-end detection of laryngeal tumors. The experimental results show that all of the three methods have good performances and single shot multibox detector (SSD) is more suitable for laryngeal tumor detection in terms of our evaluation metrics.