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VRJBro/
skin-cancer-detection

Name: skin-cancer-detection
Availability: InStock

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Skin Cancer Detection Model

This is a deep learning model designed to detect skin cancer from images. It is trained on a diverse dataset of skin lesions and uses advanced convolutional neural network (CNN) architectures to classify images as cancerous or non-cancerous. The model is highly specialized in detecting common skin cancers such as melanoma, basal cell carcinoma, and squamous cell carcinoma.

Model Details

Model Architecture: VGG16-based convolutional neural network.
Input: RGB images of skin lesions.
Output: A classification label indicating whether the lesion is cancerous or non-cancerous.
Dataset: The model was trained using a dataset from the International Skin Imaging Collaboration (ISIC). The dataset contains labeled images of different skin lesions categorized into cancerous and non-cancerous groups.

Model Performance

Accuracy: Achieved an accuracy of 97.5% on the test set.
Loss: Final test loss: 0.29.
Confusion Matrix:
Confusion Matrix

Usage

You can use this model to classify skin lesions by providing an image. Here's an example of how to use the model:

python

from transformers import pipeline

# Load the model from the Hugging Face Hub
classifier = pipeline("image-classification", model="VRJBro/skin-cancer-detection")

# Example usage
image_path = "path_to_your_image.jpg"
result = classifier(image_path)
print(result)

Limitations

This model is not a substitute for medical advice. Always consult a dermatologist or medical professional for accurate diagnosis and treatment.
The model may not perform well on images with low resolution, extreme lighting, or non-standard viewpoints.

Training Process

The model was trained using a multi-phase approach:

Data Augmentation: The images were augmented with random flips, rotations, and zooms to improve generalization.
Initial Training: The model was trained with frozen layers of the base VGG16 architecture using a learning rate of 0.001.
Fine-Tuning: The model was fine-tuned with partially unfrozen layers to boost performance.
Loss Function: The training process used sparse_categorical_crossentropy to handle the multi-class classification problem.

License

The model is released under the MIT License. You are free to use, modify, and distribute the model, provided that proper credit is given.

Citation

If you use this model in your research or applications, please cite it as follows:

@inproceedings{vrjbro_skin_cancer_detection,
  title={Skin Cancer Detection Model},
  author={VRJBro},
  year={2024},
  howpublished={\url{https://huggingface.co/VRJBro/skin-cancer-detection}},
}