CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy

TL;DR

CAVE-Net is an ensemble framework that combines deep learning and traditional classifiers to improve accuracy in detecting abnormalities in video capsule endoscopy images, demonstrating robustness and effectiveness in challenging datasets.

Contribution

The paper introduces CAVE-Net, a novel ensemble approach integrating CNNs with multiple classifiers for enhanced medical image classification accuracy.

Findings

Achieves high accuracy across challenging datasets

Demonstrates robustness in imbalanced class scenarios

Outperforms existing methods in medical image classification

Abstract

Accurate classification of medical images is critical for detecting abnormalities in the gastrointestinal tract, a domain where misclassification can significantly impact patient outcomes. We propose an ensemble-based approach to improve diagnostic accuracy in analyzing complex image datasets. Using a Convolutional Block Attention Module along with a Deep Neural Network, we leverage the unique feature extraction capabilities of each model to enhance the overall accuracy. The classification models, such as Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbors are introduced to further diversify the predictive power of proposed ensemble. By using these methods, the proposed framework, CAVE-Net, provides robust feature discrimination and improved classification results. Experimental evaluations demonstrate that the CAVE-Net achieves high accuracy and robustness across challenging and imbalanced classes, showing significant promise for broader applications in computer vision tasks.