Summary of Leveraging Spatial and Semantic Feature Extraction For Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks, by K. P. Santoso et al.
Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks
by K. P. Santoso, R. V. H. Ginardi, R. A. Sastrowardoyo, F. A. Madany
First submitted to arxiv on: 18 Mar 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 architecture combines Graph Neural Networks (GNNs) and Capsule Networks to improve skin lesion image classification performance. The GNNs capture complex patterns and relationships, while the Capsule Networks recognize spatial hierarchies within images. The hybrid model, Tiny Pyramid Vision GNN with a Capsule Network, outperforms established benchmarks on the MNIST:HAM10000 dataset, achieving 89.23% and 95.52% accuracy after 75 epochs of training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Skin lesion image classification is tricky because images have complex features and are often imbalanced. This means minority class features can be hard to learn. The study introduces a new approach that combines Graph Neural Networks (GNNs) and Capsule Networks. GNNs help with complex patterns, while Capsules recognize spatial hierarchies. They use the Tiny Pyramid Vision GNN architecture and train it on the MNIST:HAM10000 dataset. This helps diagnose skin lesions better. |
Keywords
» Artificial intelligence » Gnn » Image classification
