Bleeding classification in Wireless Capsule Endoscopy Images based on Inception-ResNet-V2 and CNNs

Wireless capsule endoscopy (WCE) is a technology that captures images of the digestive tract with a pill-sized camera. Capsule endoscopies are used to exclude or diagnose disorders such as bleeding, early symptoms of gastrointestinal cancer, abdominal pain, Crohn's disease, Celiac disease, polyps, and ulcers. However, the main cause for a capsule endoscopy is to scout for small intestine haemorrhage. Because of the technological limits, the images are low quality and feature multiple orientations due to the capsule's free mobility. In this study, we propose a technique for detecting bleeding in WCE images. We deploy a deep neural network that uses the Inception-ResNet-V2 model for its high level, combined with a low-level model that is a convolutional neural network (CNN), to attain better classification performance. The proposed methods' average accuracy is 98.5 %, with sensitivity, specificity, and precision of 98.5 %, 99 %, and 98.5 %, respectively. It clearly shows that our method outperforms state-of-the-art approaches in detecting haemorrhage.