A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images

TL;DR

This paper introduces a hybrid CNN with meta-feature learning for detecting gastrointestinal abnormalities in wireless capsule endoscopy images, achieving high accuracy and robustness across datasets.

Contribution

It proposes a novel hybrid CNN architecture with three parallel networks, including a meta-feature extraction mechanism, to improve abnormality detection in endoscopy images.

Findings

Achieved 97% accuracy on KID dataset

Achieved 98% accuracy on Kvasir-Capsule dataset

Outperformed six state-of-the-art methods

Abstract

Wireless Capsule Endoscopy is one of the most advanced non-invasive methods for the examination of gastrointestinal tracts. An intelligent computer-aided diagnostic system for detecting gastrointestinal abnormalities like polyp, bleeding, inflammation, etc. is highly exigent in wireless capsule endoscopy image analysis. Abnormalities greatly differ in their shape, size, color, and texture, and some appear to be visually similar to normal regions. This poses a challenge in designing a binary classifier due to intra-class variations. In this study, a hybrid convolutional neural network is proposed for abnormality detection that extracts a rich pool of meaningful features from wireless capsule endoscopy images using a variety of convolution operations. It consists of three parallel convolutional neural networks, each with a distinctive feature learning capability. The first network utilizes depthwise separable convolution, while the second employs cosine normalized convolution operation. A novel meta-feature extraction mechanism is introduced in the third network, to extract patterns from the statistical information drawn over the features generated from the first and second networks and its own previous layer. The network trio effectively handles intra-class variance and efficiently detects gastrointestinal abnormalities. The proposed hybrid convolutional neural network model is trained and tested on two widely used publicly available datasets. The test results demonstrate that the proposed model outperforms six state-of-the-art methods with 97\% and 98\% classification accuracy on KID and Kvasir-Capsule datasets respectively. Cross dataset evaluation results also demonstrate the generalization performance of the proposed model.