Ulcer detection in Wireless Capsule Endoscopy images using deep CNN

Abstract

Wireless Capsule Endoscopy (WCE) has been widely accepted due to its painless method of imaging the entire gastrointestinal tract. In this paper, we propose deep Convolutional Neural Network(CNN) for automatic discrimination of ulcers on different ratios of augmented datasets ranging from 1000 to 10000 WCE images comprising of ulcer and non-ulcer images. A detailed investigation of network configuration for various nodes and depth were performed. The proposed network architecture of four convolutional layers with (3*3) convolutional filters demonstrated significant improvement in terms of performance. The WCE images were obtained from publicly available WCE datasets and real-time WCE video frames. The test results were subjected to hyper-parameter optimization for various tweaking parameters such as epochs, pooling schemes, learning rate, number of layers, optimizer, activation functions and drop out scheme. The experimental results were compared with ten different machine learning classifiers, demonstrating higher prediction performance.

Keywords

• Deep learning
• Ulcer detection
• Convolutional neural network (CNN)
• Data augmentation
• Machine learning