# Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification

arXiv: https://arxiv.org/abs/2411.01652 · HTML: https://mlsanity.com/paper/2411.01652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 3 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations](https://mlsanity.com/paper/2410.19363/) (2024)
- [Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal Endoscopy](https://mlsanity.com/paper/2410.21302/) (2024)
- [CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy](https://mlsanity.com/paper/2410.20231/) (2024)
- [CASCRNet: An Atrous Spatial Pyramid Pooling and Shared Channel Residual based Network for Capsule Endoscopy](https://mlsanity.com/paper/2410.17863/) (2024)
- [Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images](https://mlsanity.com/paper/2410.18457/) (2024)
- [CapsoNet: A CNN-Transformer Ensemble for Multi-Class Abnormality Detection in Video Capsule Endoscopy](https://mlsanity.com/paper/2410.18879/) (2024)
- [CapsuleNet: A Deep Learning Model To Classify GI Diseases Using EfficientNet-b7](https://mlsanity.com/paper/2410.19151/) (2024)
- [A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERT](https://mlsanity.com/paper/2410.19944/) (2024)

## Abstract (arXiv)

In recent years, the diagnosis of gastrointestinal (GI) diseases has advanced greatly with the advent of high-tech video capsule endoscopy (VCE) technology, which allows for non-invasive observation of the digestive system. The MisaHub Capsule Vision Challenge encourages the development of vendor-independent artificial intelligence models that can autonomously classify GI anomalies from VCE images. This paper presents CNN architecture designed specifically for multiclass classification of ten gut pathologies, including angioectasia, bleeding, erosion, erythema, foreign bodies, lymphangiectasia, polyps, ulcers, and worms as well as their normal state.