Summary of Optimizing Gastrointestinal Diagnostics: a Cnn-based Model For Vce Image Classification, by Vaneeta Ahlawat et al.
Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification
by Vaneeta Ahlawat, Rohit Sharma, Urush
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel Convolutional Neural Network (CNN) architecture for multi-class classification of gastrointestinal (GI) pathologies from video capsule endoscopy (VCE) images. The model is designed to identify ten distinct gut anomalies, including angioectasia, bleeding, erosion, erythema, foreign bodies, lymphangiectasia, polyps, ulcers, and worms, as well as their normal state. This architecture aims to overcome the limitations of vendor-dependent AI models by developing a vendor-independent solution that can be used for diagnosis of GI diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors use special computers to better understand what’s going on in people’s stomachs and intestines. It uses a type of artificial intelligence called a neural network to look at pictures taken from inside the body using a tiny camera. The computer is trying to figure out what kind of problems are happening, like if there’s bleeding or polyps growing. This could help doctors diagnose diseases more accurately and give patients better treatment. |
Keywords
» Artificial intelligence » Classification » Cnn » Neural network
