Summary of Deep Learning-driven Heat Map Analysis For Evaluating Thickness Of Wounded Skin Layers, by Devakumar Gr et al.
Deep Learning-Driven Heat Map Analysis for Evaluating thickness of Wounded Skin Layers
by Devakumar GR, JB Kaarthikeyan, Dominic Immanuel T, Sheena Christabel Pravin
First submitted to arxiv on: 19 Nov 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 Medium Difficulty summary: This paper introduces a novel non-invasive method for measuring wound depth using deep learning techniques and heatmap analysis. The approach is based on classifying skin layers in labeled images, allowing for the distinction between scars, wounds, and healthy skin. The Heatmap generator VGG16 was used to enhance tissue layer visibility, and ResNet18 with early stopping techniques achieved a high accuracy rate of 97.67%. A comparison of models ResNet18, VGG16, DenseNet121, and EfficientNet showed that both EfficientNet and ResNet18 attained similar accuracy rates of nearly 95.35%. The paper also explores hyperparameter tuning for EfficientNet and ResNet18, revealing significant variations in accuracy with different learning rates. This research holds promise for improving clinical diagnosis and treatment planning through non-invasive wound assessment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This study is about creating a new way to measure the depth of wounds without touching them. Doctors need this information to help heal wounds faster and better. The team used special computer programs called deep learning algorithms to analyze pictures of skin layers. They found that certain types of images, like scars or healthy skin, can be easily identified. This technology could one day help doctors make better decisions about how to treat patients with wounds. |
Keywords
» Artificial intelligence » Deep learning » Early stopping » Hyperparameter
