Summary of Mpox Detection Advanced: Rapid Epidemic Response Through Synthetic Data, by Yudara Kularathne et al.
Mpox Detection Advanced: Rapid Epidemic Response Through Synthetic Data
by Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya, Prarththanan Sothyrajah, Thanveer Ahamed, Dinuka Wijesundara
First submitted to arxiv on: 25 Jul 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 research introduces a novel approach to rapidly developing disease detection models using computer vision by constructing a comprehensive model that detects Mpox lesions using synthetic data. The study trains and tests a vision model on this dataset, achieving promising results with high accuracy rates, precision, recall, and F1-Scores for identifying Mpox cases and other skin disorders. The proposed SynthVision methodology has the potential to develop accurate computer vision models with minimal data input for future medical emergencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps create quick and reliable disease detection models using computer vision by building a model that detects Mpox lesions from synthetic images. Scientists made fake pictures of different skin types and used them to train a machine learning model. The results are good, showing the model can accurately identify Mpox cases and other skin conditions. This could help doctors quickly diagnose diseases in emergency situations. |
Keywords
» Artificial intelligence » Machine learning » Precision » Recall » Synthetic data
