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--- |
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license: mit |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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tags: |
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- medical |
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--- |
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# **Skin Cancer Detection Model** |
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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. |
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--- |
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## **Model Details** |
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- **Model Architecture**: VGG16-based convolutional neural network. |
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- **Input**: RGB images of skin lesions. |
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- **Output**: A classification label indicating whether the lesion is cancerous or non-cancerous. |
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- **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. |
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--- |
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## **Model Performance** |
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- **Accuracy**: Achieved an accuracy of **97.5%** on the test set. |
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- **Loss**: Final test loss: **0.29**. |
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- **Confusion Matrix**:  |
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--- |
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## **Usage** |
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You can use this model to classify skin lesions by providing an image. Here's an example of how to use the model: |
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```python |
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from transformers import pipeline |
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# Load the model from the Hugging Face Hub |
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classifier = pipeline("image-classification", model="VRJBro/skin-cancer-detection") |
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# Example usage |
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image_path = "path_to_your_image.jpg" |
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result = classifier(image_path) |
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print(result) |
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``` |
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### **Limitations** |
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- This model is **not a substitute for medical advice**. Always consult a dermatologist or medical professional for accurate diagnosis and treatment. |
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- The model may not perform well on images with low resolution, extreme lighting, or non-standard viewpoints. |
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--- |
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## **Training Process** |
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The model was trained using a multi-phase approach: |
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1. **Data Augmentation**: The images were augmented with random flips, rotations, and zooms to improve generalization. |
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2. **Initial Training**: The model was trained with frozen layers of the base VGG16 architecture using a learning rate of **0.001**. |
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3. **Fine-Tuning**: The model was fine-tuned with partially unfrozen layers to boost performance. |
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4. **Loss Function**: The training process used `sparse_categorical_crossentropy` to handle the multi-class classification problem. |
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--- |
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## **License** |
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The model is released under the **MIT License**. You are free to use, modify, and distribute the model, provided that proper credit is given. |
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--- |
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## **Citation** |
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If you use this model in your research or applications, please cite it as follows: |
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``` |
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@inproceedings{vrjbro_skin_cancer_detection, |
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title={Skin Cancer Detection Model}, |
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author={VRJBro}, |
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year={2024}, |
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howpublished={\url{https://huggingface.co/VRJBro/skin-cancer-detection}}, |
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} |
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``` |
