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.
@article{arxiv.2410.20231,
title = {CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy},
author = {Ishita Harish and Saurav Mishra and Neha Bhadoria and Rithik Kumar and Madhav Arora and Syed Rameem Zahra and Ankur Gupta},
journal= {arXiv preprint arXiv:2410.20231},
year = {2024}
}