detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2411.01652
Tema
Computer Science - Computer Vision... Computer Science - Artificial Inte...Autor
Ahlawat, Vaneeta Sharma, Rohit UrushCategoría
Computer Science
Año
2024
fecha de cotización
6/11/2024
Palabras clave
computer vceResumen
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
The Differential Weights of Motivational and Task Performance Measures on Medial and Lateral Frontal Neural Activity
related frontal task factors behavioral activity difficulty
