WCEbleedGen: A wireless capsule endoscopy dataset and its benchmarking for automatic bleeding classification, detection, and segmentation
Journal:
arXiv
Published Date:
Aug 22, 2024
Abstract
Computer-based analysis of Wireless Capsule Endoscopy (WCE) is crucial.
However, a medically annotated WCE dataset for training and evaluation of
automatic classification, detection, and segmentation of bleeding and
non-bleeding frames is currently lacking. The present work focused on
development of a medically annotated WCE dataset called WCEbleedGen for
automatic classification, detection, and segmentation of bleeding and
non-bleeding frames. It comprises 2,618 WCE bleeding and non-bleeding frames
which were collected from various internet resources and existing WCE datasets.
A comprehensive benchmarking and evaluation of the developed dataset was done
using nine classification-based, three detection-based, and three
segmentation-based deep learning models. The dataset is of high-quality, is
class-balanced and contains single and multiple bleeding sites. Overall, our
standard benchmark results show that Visual Geometric Group (VGG) 19, You Only
Look Once version 8 nano (YOLOv8n), and Link network (Linknet) performed best
in automatic classification, detection, and segmentation-based evaluations,
respectively. Automatic bleeding diagnosis is crucial for WCE video
interpretations. This diverse dataset will aid in developing of real-time,
multi-task learning-based innovative solutions for automatic bleeding diagnosis
in WCE. The dataset and code are publicly available at
https://zenodo.org/records/10156571 and
https://github.com/misahub2023/Benchmarking-Codes-of-the-WCEBleedGen-dataset.
