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The project leverages convolutional neural networks (CNNs) to classify skin lesions from dermatoscopic images, assisting in early detection of conditions like melanoma.\n\n🚀 Features\n- Image preprocessing and augmentation\n- CNN-based classification model (custom and/or transfer learning with models like ResNet, EfficientNet, etc.)\n- Evaluation metrics (accuracy, confusion matrix, precision, recall, F1-score)\n- Visualizations for training/validation accuracy and loss\n- Web application built with Django for user-friendly diagnosis\n- Sections like FAQ, Precaution Advisory, and Contact Us for a complete healthcare experience\n\n🧪 Dataset\nThe model is trained on the HAM10000 dataset, which contains over 10,000 labeled dermatoscopic images across 7 skin lesion types.\n\n🛠️ Technologies Used\n- Python, TensorFlow/Keras\n- OpenCV, NumPy, Matplotlib\n- Django (for web deployment)\n- SQLite (for backend storage, if applicable)\n\n## 📬 Contact\n\nIf you have any questions or suggestions, feel free to reach out to me at:  \n**tanmayeepatil1620@gmail.com**\n![f1](https://github.com/user-attachments/assets/de5f3ee8-9a4f-45ce-bf98-4e2f896e4197)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftanmayee2010%2Fskin-lesion-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftanmayee2010%2Fskin-lesion-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftanmayee2010%2Fskin-lesion-detection/lists"}