Summary of Web-based Melanoma Detection, by Sanghyuk Kim et al.
Web-based Melanoma Detection
by SangHyuk Kim, Edward Gaibor, Daniel Haehn
First submitted to arxiv on: 22 Mar 2024
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 The proposed unified melanoma classification approach enables fair comparison of various deep learning architectures and datasets, leading to a lightweight model deployable to the web-based MeshNet architecture named Mela-D. This approach reduces parameters 24x while maintaining an analogous 88.8% accuracy comparable with ResNet50 on previously unseen images, making it efficient and accurate for melanoma detection in real-world settings that can run on consumer-level hardware. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study introduces a new way to detect melanoma using deep learning models. It’s important because early detection of melanoma can save lives. The researchers developed a special approach that allows them to compare many different deep learning models and datasets, which helps to find the best model for detecting melanoma. This approach is fast and accurate and can run on regular computers. |
Keywords
* Artificial intelligence * Classification * Deep learning
