Summary of Diagnosis Of Diabetic Retinopathy Using Machine Learning & Deep Learning Technique, by Eric Shah et al.
Diagnosis of diabetic retinopathy using machine learning & deep learning technique
by Eric Shah, Jay Patel, Mr.Vishal Katheriya, Parth Pataliya
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 A novel machine learning-based method is proposed for detecting fundus images and diagnosing various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. The approach leverages object detection using YOLO_V8 to locate regions of interest (ROIs) like optic disc, cup, and lesions, followed by SVM classification algorithms to classify ROIs into different disease stages based on the presence or absence of pathological signs. This method achieves 84% accuracy and efficiency for fundus detection, with potential applications in remote areas for retinal fundus disease triage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is developed to help doctors diagnose eye problems using computer vision and machine learning. The system uses a special algorithm called YOLO_V8 to find important parts of the eye picture, such as the optic disc and cup, and then classifies these areas into different stages of disease based on what’s seen. This method works well, achieving 84% accuracy, and could be useful for quickly diagnosing eye problems in places where it might not be easy to get medical attention. |
Keywords
» Artificial intelligence » Attention » Classification » Machine learning » Object detection
