Dokumentdetails
ID
oai:arXiv.org:2411.01652
Thema
Computer Science - Computer Vision... Computer Science - Artificial Inte...Autor
Ahlawat, Vaneeta Sharma, Rohit UrushKategorie
Computer Science
Jahr
2024
Auflistungsdatum
06.11.2024
Schlüsselwörter
computer vceZusammenfassung
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
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