Classification of anatomical landmarks from upper gastrointestinal endoscopic images^⋆

In this paper, we propose a framework that automatically classifies anatomical landmarks of Upper GastroIntestinal Endoscopy (UGIE). This framework aims to select the best deep neural network in terms of both criteria of classification performances and computational costs. We investigate two lightweight deep neural networks that are ResNet-18, MobileNet-V2 to learn hidden discriminant features for multi classification task. In addition, because convolutional neural networks (CNNs) are data hungry, we examine various data augmentation (DA) techniques such as Brightness and Contrast Transformation (BaC), Geometric Transformation (GeoT), and Variational Auto-Encoder (VAE). Impacts of these DA schemes are evaluated for both CNN models. The experiments are conducted on a self collected dataset of 3700 endoscopic images which contains 10 anatomical landmarks of UGIE. The results show outstanding performances of both models thanks to DA techniques compared to the original data usage. The best sensitivity is 97.43% and specificity is 99.71% using MobileNet-V2 with Geometric Transformation based DA technique at a frame-rate of 21fps. These results highlight the best model which has significant potential for developing computer-aided esophagogastroduodenoscopy (EGD) diagnostic systems.