Gastrointestinal image classification based on VGG16 and transfer learning

Abstract

Investigational procedures and medical diagnosis can be greatly improved by opting for detecting automatically abnormalities and anatomical landmarks in medical images. However, this remains a challenging task and still unexplored field. This paper aims to investigate the capabilities of a pretrained deep convolutional neural network VGG-16 model for images categorization with transfer learning containing anatomical landmarks, pathological finding and endoscopic procedures. Data augmentation is also performed to highlight the importance of data size for deep models. The accuracies achieved before and after data augmentation are 96.9% and 98.8% respectively.