UPolySeg: A U-Net-Based Polyp Segmentation Network Using Colonoscopy Images

Colonoscopy is a gold standard procedure for tracking the lower gastrointestinal region. A colorectal polyp is one such condition that is detected through colonoscopy. Even though technical advancements have improved the early detection of colorectal polyps, there is still a high percentage of misses due to various factors. Polyp segmentation can play a significant role in the detection of polyps at the early stage and can thus help reduce the severity of the disease. In this work, the authors implemented several image pre-processing techniques such as coherence transport and contrast limited adaptive histogram equalization (CLAHE) to handle different challenges in colonoscopy images. The processed image was then segmented into a polyp and normal pixel using a U-Net-based deep learning segmentation model named UPolySeg. The main framework of UPolySeg has an encoder-decoder section with feature concatenation in the same layer as the encoder-decoder along with the use of dilated convolution. The model was experimentally verified using the publicly available Kvasir-SEG dataset, which gives a global accuracy of 96.77%, a dice coefficient of 96.86%, an IoU of 87.91%, a recall of 95.57%, and a precision of 92.29%. The new framework for the polyp segmentation implementing UPolySeg improved the performance by 1.93% compared with prior work.