An AI-powered skin lesion classification system leveraging advanced Convolutional Neural Networks to support early screening of skin cancer. Multiple models have been compared to identify the best architecture for accurate diagnosis.
- Classify skin lesions using dermatoscopic images
- Compare performance of multiple transfer-learning architectures
- Support medical experts in early cancer risk detection
| Model | Status | Remarks |
|---|---|---|
| VGG-16 | ✔ | Transfer learning baseline |
| VGG-19 | ✔ | Improved feature extraction |
| ResNet50 | ✔ | Strong performance on medical images |
| DenseNet169 | ✔ | Captures deep hierarchical features |
| EfficientNetB0 | ✔ | Efficient & high accuracy |
| InceptionV3 | ✔ | Good at multi-scale feature learning |
| ESRGAN | ✔ | Used for resolution enhancement |
📌 All training notebooks included inside the repository.
📁 Skin-cancer
├─ content/ # Image content / visual assets (optional)
├─ VGG-16.ipynb
├─ VGG-19.ipynb
├─ ResNet50.ipynb
├─ DenseNet169.ipynb
├─ InceptionV3.ipynb
├─ EfficientNetB0.ipynb
├─ ESRGAN.ipynb
└─ README.md
Dermatoscopic skin lesion dataset:
- HAM10000 / ISIC (commonly used in research)
(You can add dataset link here if public)
- Accuracy
- Precision, Recall, F1-Score
- ROC-AUC Score
- Confusion Matrix
- Grad-CAM Visual Explanations 🔥
These results will help determine the best model for deployment.
# Install dependencies (suggested)
pip install tensorflow keras numpy pandas matplotlib scikit-learn opencv-python
pip install jupyter
jupyter notebookExecute any notebook like:
VGG-16.ipynb
- Deploy as a web app (Streamlit or Gradio UI)
- Add Explainability module (Grad-CAM heatmaps)
- Clinical-level evaluation with dermatologists
- Export to ONNX / TensorRT for real-time inference
This project is for research & educational purposes only. It is not a replacement for professional medical diagnosis.
Amitabh Thakur AI/ML Engineer | CSE (AI & ML) @ Dayananda Sagar University Founder — Humans Care Foundation Bangalore, India
🌟 Contributions, forks & research collaboration are welcome!