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Description: Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. However, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. In this respect, we present Kvasir-Capsule, a large VCE dataset collected from examinations at Hospitals in Norway. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around detected anomalies from 14 different classes of findings. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. Initial work demonstrates the potential benefits ofAI-based computer-assisted diagnosis systems for VCE. However, they also show that there is great potential for improvements, and the Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order for VCE technology to reach its true potential.

License: CC-By Attribution 4.0 International

Has supplemental materials for Kvasir-Capsule, a video capsule endoscopy dataset on OSF Preprints

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