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

TL;DR

This paper introduces a CNN-based model tailored for classifying ten different gastrointestinal conditions from VCE images, advancing non-invasive GI diagnostics with AI.

Contribution

It presents a novel CNN architecture specifically designed for multiclass classification of GI anomalies in VCE images, addressing the need for vendor-independent AI tools.

Findings

Achieved accurate classification of ten GI pathologies

Demonstrated robustness across diverse VCE images

Improved diagnostic efficiency in GI disease detection

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.