Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification
This addresses the need for vendor-independent AI models to improve gastrointestinal diagnostics, but it appears incremental as it applies an existing method (CNN) to a new dataset.
The paper tackled the problem of classifying gastrointestinal anomalies from video capsule endoscopy images, presenting a CNN architecture that achieved multiclass classification for ten pathologies and normal states.
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.