# CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy

arXiv: https://arxiv.org/abs/2410.20231 · HTML: https://mlsanity.com/paper/2410.20231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy](https://mlsanity.com/paper/2408.04940/) (2024)
- [Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network](https://mlsanity.com/paper/2206.08298/) (2022)
- [AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for Detecting Multi-generator Machine-generated Text](https://mlsanity.com/paper/2404.00950/) (2024)
- [Learning Gaussian Data Augmentation in Feature Space for One-shot Object Detection in Manga](https://mlsanity.com/paper/2410.05935/) (2024)
- [Example-based Explanations for Random Forests using Machine Unlearning](https://mlsanity.com/paper/2402.05007/) (2024)

## Built on by (most cited first, 2 of 2)

- [Divide-then-Diagnose: Weaving Clinician-Inspired Contexts for Ultra-Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2604.21814/) (2026)
- [Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification](https://mlsanity.com/paper/2411.01652/) (2024)

## Abstract (arXiv)

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.