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

Source: [Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification](<https://www.sota2.com/research/paper/68fa75841e209131cea6854d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:35:52.229Z

Authors: Vaneeta Ahlawat, Rohit Sharma, Urush

Published: 2024-11-03T18:30:37.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.01652>)

[PDF](<https://arxiv.org/pdf/2411.01652.pdf>)