Détail du document
Identifiant
oai:arXiv.org:2411.01652
Sujet
Computer Science - Computer Vision... Computer Science - Artificial Inte...Auteur
Ahlawat, Vaneeta Sharma, Rohit UrushCatégorie
Computer Science
Année
2024
Date de référencement
06/11/2024
Mots clés
computer vceRésumé
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.
;Comment: 11 pages, 7 figuers
Ahlawat, Vaneeta,Sharma, Rohit,Urush, 2024, Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification
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