Summary of Integrating Deep Feature Extraction and Hybrid Resnet-densenet Model For Multi-class Abnormality Detection in Endoscopic Images, by Aman Sagar et al.
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images
by Aman Sagar, Preeti Mehta, Monika Shrivastva, Suchi Kumari
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The paper proposes a deep learning framework for multi-class classification of gastrointestinal abnormalities in Video Capsule Endoscopy (VCE) frames. The aim is to automate the identification of ten GI abnormality classes using an ensemble of DenseNet and ResNet architectures. The model achieves an overall accuracy of 94% on a well-structured dataset, with precision scores ranging from 0.56 for erythema to 1.00 for worms, and recall rates peaking at 98% for normal findings. The study highlights the importance of robust data preprocessing techniques in enhancing model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a tool that helps doctors diagnose gastrointestinal problems using video recordings. It uses special AI models to find different types of abnormalities, like bleeding or ulcers. The tool is very good at doing this, getting 94% correct most of the time. It’s also really good at finding things that are normal. |
Keywords
» Artificial intelligence » Classification » Deep learning » Precision » Recall » Resnet
