đ Skin Cancer Image Classification Model
This model is designed to classify skin cancer images into multiple categories, providing valuable assistance in skin cancer diagnosis.
đ Quick Start
This README provides a comprehensive overview of a skin cancer image classification model, including its architecture, dataset, training process, evaluation metrics, and results.
⨠Features
- Multiclass Classification: Capable of classifying skin cancer images into various categories such as benign keratosis - like lesions, basal cell carcinoma, etc.
- Vision Transformer Architecture: Utilizes the Vision Transformer (ViT) architecture, pre - trained on ImageNet21k for better feature extraction.
- Modified Classification Head: The classification head is customized to fit the skin cancer classification task.
đĻ Installation
No specific installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
No code examples are provided in the original document, so this section is skipped.
đ Documentation
Model Overview
| Property |
Details |
| Model Type |
Vision Transformer (ViT) |
| Pre - trained Model |
Google's ViT with 16x16 patch size, trained on ImageNet21k dataset |
| Modified Part |
The classification head is replaced for skin cancer classification |
Dataset
- Dataset Name: Skin Cancer Dataset
- Source: Marmal88's Skin Cancer Dataset on Hugging Face
- Classes: Benign keratosis - like lesions, Basal cell carcinoma, Actinic keratoses, Vascular lesions, Melanocytic nevi, Melanoma, Dermatofibroma
Training
| Property |
Details |
| Optimizer |
Adam optimizer with a learning rate of 1e - 4 |
| Loss Function |
Cross - Entropy Loss |
| Batch Size |
32 |
| Number of Epochs |
5 |
Evaluation Metrics
- Train Loss: Average loss over the training dataset
- Train Accuracy: Accuracy over the training dataset
- Validation Loss: Average loss over the validation dataset
- Validation Accuracy: Accuracy over the validation dataset
Results
| Epoch |
Train Loss |
Train Accuracy |
Val Loss |
Val Accuracy |
| 1/5 |
0.7168 |
0.7586 |
0.4994 |
0.8355 |
| 2/5 |
0.4550 |
0.8466 |
0.3237 |
0.8973 |
| 3/5 |
0.2959 |
0.9028 |
0.1790 |
0.9530 |
| 4/5 |
0.1595 |
0.9482 |
0.1498 |
0.9555 |
| 5/5 |
0.1208 |
0.9614 |
0.1000 |
0.9695 |
Conclusion
The model shows good performance in classifying skin cancer images. Further fine - tuning or experimentation may enhance its performance on this task.
đ§ Technical Details
No specific technical implementation details (more than 50 words) are provided in the original document, so this section is skipped.
đ License
The model is licensed under the Apache - 2.0 license.
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