Automated Classification of Video Capsule EndoscopyAbnormalities Using DINOv2

This project develops a deep learning model utilizing the DINOv2 architecture toautomatically classify abnormalities detected in video capsule endoscopy (VCE) frames.The focus is on ten specific abnormalities, assisting healthcare professionals in diagnosinggastrointestinal disorders efficiently.

Publication Details

Journal
Figshare
Published
2024-10-25
DOI
https://doi.org/10.6084/m9.figshare.27307797
Primary Topic
Gastrointestinal Bleeding Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

Automated Classification of Video Capsule EndoscopyAbnormalities Using DINOv2

Figshare
Gastrointestinal Bleeding Diagnosis and Treatment
article

Automated Classification of Video Capsule EndoscopyAbnormalities Using DINOv2

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Abstract

This project develops a deep learning model utilizing the DINOv2 architecture toautomatically classify abnormalities detected in video capsule endoscopy (VCE) frames.The focus is on ten specific abnormalities, assisting healthcare professionals in diagnosinggastrointestinal disorders efficiently.

Figshare
Openalex Percentile: Top 46%
Gastrointestinal Bleeding Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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Automated Classification of Video Capsule EndoscopyAbnormalities Using DINOv2 · Figshare (2024) | TGRS Research Map | TGRS