Create README.md

README.md

@@ -0,0 +1,84 @@
+---
+license: mit
+metrics:
+- accuracy
+pipeline_tag: image-classification
+tags:
+- medical
+---
+# **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](./confusion_matrix.png)
+
+
+---
+
+## **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:
+
+1. **Data Augmentation**: The images were augmented with random flips, rotations, and zooms to improve generalization.
+2. **Initial Training**: The model was trained with frozen layers of the base VGG16 architecture using a learning rate of **0.001**.
+3. **Fine-Tuning**: The model was fine-tuned with partially unfrozen layers to boost performance.
+4. **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}},
+}
+```