Segmentation of Bleeding Regions in Wireless Capsule Endoscopy for Detection of Informative Frames

TL;DR

This paper explores simplifying neural networks for automatic bleeding detection in wireless capsule endoscopy, balancing computational efficiency and accuracy to enable inside-device implementation.

Contribution

It introduces simplified CNN and MLP models optimized for low computational cost while maintaining high detection accuracy for bleeding regions.

Findings

Simplified networks significantly reduce computational operations.

Both models achieve AUC > 0.97 in bleeding detection.

Simplified MLP offers a good trade-off between simplicity and performance.

Abstract

Wireless capsule endoscopy (WCE) is an effective mean for diagnosis of gastrointestinal disorders. Detection of informative scenes in WCE video could reduce the length of transmitted videos and help the diagnosis procedure. In this paper, we investigate the problem of simplification of neural networks for automatic bleeding region detection inside capsule endoscopy device. Suitable color channels are selected as neural networks inputs, and image classification is conducted using a multi-layer perceptron (MLP) and a convolutional neural network (CNN) separately. Both CNN and MLP structures are simplified to reduce the number of computational operations. Performances of two simplified networks are evaluated on a WCE bleeding image dataset using the DICE score. Simulation results show that applying simplification methods on both MLP and CNN structures reduces the number of computational operations significantly with AUC greater than 0.97. Although CNN performs better in comparison with simplified MLP, the simplified MLP segments bleeding regions with a significantly smaller number of computational operations. Concerning the importance of having a simple structure or a more accurate model, each of the designed structures could be selected for inside capsule implementation.