Convolutional Neural Network for Automated Colorectal Polyp Semantic Segmentation on Colonoscopy Frames

Abstract

Colorectal cancer is one of the deadliest cancer types worldwide. Therefore, an early detection is crucial to winning the fight against this disease. In this work, to help ease polyp detection, we present a fully convolutional network for colorectal polyp semantic segmentation (FCN-SEG4). This approach uses VGG16 as the backbone for feature extraction, followed by a series of transpose convolutions to get an accurate semantic segmentation. After training the model on the CVC-clinicDB dataset, an overall precision of 86.75% was reached. We trained FCN -SEG4 using other datasets to study the effect it may have on the results. This proposal proved good potential with room for improvement especially when it comes to performance speed.