Polyp Detection in Gastrointestinal Images using Faster Regional Convolutional Neural Network

Abstract

Colorectal cancer is the third most frequently diagnosed malignancy in the world. To prevent this disease, polyps, the principal precursor, are removed during a colonoscopy. Automatic detection of polyps in this technique could play an important role to assist doctors for achieving an accurate diagnosis. In this work, we apply a state-of-the-art Deep Learning algorithm called Faster Regional Convolutional Neural Network to each colonoscopy frame in order to detect the presence of polyps. The proposed detection system contains two main stages: (1) processing of the colonoscopy frames for training and testing datasets generation, where artifacts are extracted and the number of images in the dataset is augmented; and (2) the Neural Network model, which performs feature extraction over the frames in order to detect polyps within the frames. After training the algorithm under different conditions, our result shows that the proposed system detection has a precision of 80.31%, a recall of 75.37%, an accuracy of 71.99% and a specificity of 65.70%.