Look-behind fully convolutional neural network for computer-aided endoscopy

Abstract

In this paper, we propose a novel Fully Convolutional Neural Network (FCN) architecture aiming to aid the detection of abnormalities, such as polyps, ulcers and blood, in gastrointestinal (GI) endoscopy images. The proposed architecture, named Look-Behind FCN (LB-FCN), is capable of extracting multi-scale image features by using blocks of parallel convolutional layers with different filter sizes. These blocks are connected by Look-Behind (LB) connections, so that the features they produce are combined with features extracted from behind layers, thus preserving the respective information. Furthermore, it has a smaller number of free parameters than conventional Convolutional Neural Network (CNN) architectures, which makes it suitable for training with smaller datasets. This is particularly useful in medical image analysis, since data availability is usually limited due to ethicolegal constraints. The performance of LB-FCN is evaluated on both flexible and wireless capsule endoscopy datasets, reaching 99.72% and 93.50%, in terms of Area Under receiving operating Characteristic (AUC) respectively. © 2018 Elsevier Ltd