Gastrointestinal diseases classification based on deep learning and transfer learning mechanism

Wireless capsule endoscopy (WCE) is a non-surgical diagnostic procedure enabling the examination of the whole human gastrointestinal tract. Thus, a patient swallows a capsule that travels down the human digestive system and a camera captures wirelessly thousands of images that are transmitted to an external recording device. The diagnosis of these images need a specialist who can identify gastrointestinal abnormalities and it is very time-consuming. Recently, artificial intelligence and deep learning techniques aim to automate disease diagnosis and identification of tumors in the gastrointestinal tract (GI) such as polyps, ulcers and bleeding, etc. In this paper, a deep learning method is proposed for gastrointestinal disease classification. The pre-trained model ResNet50 is fine-tuned through transfer learning to extract deep features from WCE images. The proposed algorithm is trained and tested on the publicly available dataset k-vasir capsule, which contains 14 different classes of gastrointestinal anomalies.