Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Ahlawat, Vaneeta, Sharma, Rohit, Urush
Language: English
Subfield: Gastroenterology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4404352805