Summary of Classification Of Diabetic Retinopathy Using Pre-trained Deep Learning Models, by Inas Al-kamachy (karlstad University et al.
Classification of Diabetic Retinopathy using Pre-Trained Deep Learning Models
by Inas Al-Kamachy, Reza Hassanpour, Roya Choupani
First submitted to arxiv on: 29 Mar 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 The proposed Computer-Aided Diagnosis (CAD) system for diabetic retinopathy classification uses Convolutional Neural Networks (CNNs) with pre-trained deep learning models fine-tuned on fundus images of diabetic retinopathy. The system is trained on 350x350x3 and 224x224x3 resolution images, achieving Area Under the Curve (AUC) values ranging from 0.50 to 0.69 for various CNN architectures tested on the Kaggle platform. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This CAD system helps diagnose diabetic retinopathy in people aged 20-70, which is a leading cause of blindness globally. The system uses deep learning models and fine-tuning techniques to classify retinal images into five categories: Normal, Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR). |
Keywords
» Artificial intelligence » Auc » Classification » Cnn » Deep learning » Fine tuning
