Summary of Mobile Application For Oral Disease Detection Using Federated Learning, by Shankara Narayanan V et al.
Mobile Application for Oral Disease Detection using Federated Learning
by Shankara Narayanan V, Sneha Varsha M, Syed Ashfaq Ahmed, Guruprakash J
First submitted to arxiv on: 27 Oct 2023
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 In this research paper, a Federated Learning (FL) approach is employed for object detection in oral images, ensuring data privacy by processing the data locally on devices rather than a central server. The proposed system, OralH, utilizes the YOLOv8 model to identify oral hygiene issues and diseases through mobile app-based mouth scans. The app provides users with quick insights into their oral health and alerts them to potential concerns or diseases, while also offering localized dental clinic information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where you can check your oral health just by taking a selfie! This innovative mobile app uses artificial intelligence (AI) to detect any issues or diseases in your mouth. The app is so clever that it can even find problems before they become serious. And the best part? It’s totally private and secure, thanks to special technology called Federated Learning. |
Keywords
* Artificial intelligence * Federated learning * Object detection
