This repository contains a complete end-to-end solution for Skin Lesion Detection using deep learning techniques. The project leverages convolutional neural networks (CNNs) to classify skin lesions from dermatoscopic images, assisting in early detection of conditions like melanoma.
🚀 Features
- Image preprocessing and augmentation
- CNN-based classification model (custom and/or transfer learning with models like ResNet, EfficientNet, etc.)
- Evaluation metrics (accuracy, confusion matrix, precision, recall, F1-score)
- Visualizations for training/validation accuracy and loss
- Web application built with Django for user-friendly diagnosis
- Sections like FAQ, Precaution Advisory, and Contact Us for a complete healthcare experience
🧪 Dataset The model is trained on the HAM10000 dataset, which contains over 10,000 labeled dermatoscopic images across 7 skin lesion types.
🛠️ Technologies Used
- Python, TensorFlow/Keras
- OpenCV, NumPy, Matplotlib
- Django (for web deployment)
- SQLite (for backend storage, if applicable)
If you have any questions or suggestions, feel free to reach out to me at:
[email protected]
