Summary of Diabetic Retinopathy Detection Using Quantum Transfer Learning, by Ankush Jain et al.
Diabetic Retinopathy Detection Using Quantum Transfer Learning
by Ankush Jain, Rinav Gupta, Jai Singhal
First submitted to arxiv on: 2 May 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 methodology leverages Quantum Transfer Learning to detect Diabetic Retinopathy (DR) from retina fundus images. The approach combines classical neural networks for feature extraction with a Variational Quantum Classifier for classification. The model is trained on the APTOS 2019 Blindness Detection dataset and achieves an accuracy of 97% using ResNet-18. This hybrid quantum model demonstrates remarkable results, showcasing the potential of quantum computing to perform tasks with power and efficiency unattainable by classical computers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Detecting Diabetic Retinopathy (DR) at an early stage can prevent vision impairment. A new approach uses Quantum Transfer Learning to detect DR from retina fundus images. This method combines computer-aided detection with quantum computing, making it more efficient and cost-effective than traditional methods. The model is trained on a dataset of retina fundus images and achieves high accuracy in detecting DR. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Resnet » Transfer learning
