Classification of Wireless Capsule Endoscopy Bleeding Images using Deep Neural Network

Wireless capsule endoscopy (WCE) is an innovative video technique that allows a non-invasive visual inspection of the whole gastrointestinal system. The digestive tract anomalies must be identified in advance to treat them before they turn into hazardous cancers. Detection and classification of digestive tract associated anomalies have been difficult because of various factors like differences in lesion form and color, level of illumination or lighting, etc. Existing approaches based on non-deep learning methods include a manually designed feature extraction step, which is less efficient since these manually designed features may lose essential information and cannot be optimised because they are not part of an end-to-end learning system. This paper provides a new way of classification between bleeding and non-bleeding classes of wireless capsule endoscopy images. The proposed method makes use of a simple Deep Convolutional Neural Network which consists of six convolutional layers alternated with max-pooling layers and this technique is compared with existing ones in terms of different performance metrics. This approach boosts diagnostic efficiency and gives doctors a huge amount of support.