Influence of color correction on pathology detection in Capsule Endoscopy
Journal:
arXiv
Published Date:
Jan 31, 2025
Abstract
Pathology detection in Wireless Capsule Endoscopy (WCE) using deep learning
has been explored in the recent past. However, deep learning models can be
influenced by the color quality of the dataset used to train them, impacting
detection, segmentation and classification tasks. In this work, we evaluate the
impact of color correction on pathology detection using two prominent object
detection models: Retinanet and YOLOv5. We first generate two color corrected
versions of a popular WCE dataset (i.e., SEE-AI dataset) using two different
color correction functions. We then evaluate the performance of the Retinanet
and YOLOv5 on the original and color corrected versions of the dataset. The
results reveal that color correction makes the models generate larger bounding
boxes and larger intersection areas with the ground truth annotations.
Furthermore, color correction leads to an increased number of false positives
for certain pathologies. However, these effects do not translate into a
consistent improvement in performance metrics such as F1-scores, IoU, and AP50.
The code is available at https://github.com/agossouema2011/WCE2024. Keywords:
Wireless Capsule Endoscopy, Color correction, Retinanet, YOLOv5, Detection
