MelanoSense is an advanced AI-driven screening platform designed to facilitate the early detection of melanoma, one of the most aggressive and life-threatening forms of skin cancer. The system functions as a clinical decision support tool, leveraging a hybrid deep learning pipeline to analyze dermoscopic images of skin lesions and identify patterns associated with malignant melanoma. By integrating artificial intelligence with medical imaging analysis, MelanoSense aims to assist healthcare professionals in performing rapid preliminary assessments of suspicious skin lesions. The platform was developed as part of a digital healthcare initiative aimed at improving melanoma awareness and supporting dermatological screening in Bangladesh, where access to specialized dermatological services is often limited, particularly in rural and underserved regions. Through AI-assisted analysis, the system helps bridge the gap between early symptom recognition and timely clinical evaluation. MelanoSense features a professional medical dashboard that allows users to securely upload dermoscopic skin lesion images and initiate automated AI-powered diagnostic analysis. The underlying model processes the image through a trained deep learning architecture capable of extracting complex visual features related to asymmetry, border irregularity, color variation, and structural abnormalities commonly associated with melanoma. After analysis, the platform generates a comprehensive diagnostic report designed to support clinical interpretation. The report includes: Predicted melanoma risk level (low, moderate, or high risk) Model confidence score, indicating the reliability of the prediction Grad-CAM heatmap visualization, which highlights the specific regions of the lesion image that influenced the model’s decision, enhancing transparency and explainability of the AI system A downloadable clinical PDF report, which can be stored, shared, or used as part of patient documentation By combining artificial intelligence, medical image analysis, and explainable AI techniques, MelanoSense provides an accessible, efficient, and interpretable tool that can support dermatologists and healthcare professionals in early melanoma risk assessment and screening workflows.
| Layer | Technology |
|---|---|
| Backend | Django 6.x (Python) |
| AI / Deep Learning | TensorFlow / Keras — CNN for image feature extraction |
| Machine Learning | XGBoost — Metadata-based risk classification |
| Image Processing | Pillow, OpenCV — Image manipulation & Grad-CAM overlays |
| PDF Reports | xhtml2pdf — Clinical diagnostic report generation |
| Frontend | HTML5, CSS3, JavaScript, Cropper.js |
| Database | SQLite (default) |
| Serialization | joblib — Model & label encoder persistence |
The model was trained using the HAM10000 dataset (The Human Against Machine with 10000 training images) — a large collection of multi-source dermatoscopic images of common pigmented skin lesions.
The application utilizes a high-performance Hybrid Model combining:
- CNN (Convolutional Neural Networks): Processes lesion images via a TensorFlow/Keras pipeline for visual feature extraction.
- XGBoost: Analyzes patient metadata (age, sex, localization) for optimized risk classification.
- Hybrid Inference: A weighted ensemble (70% CNN, 30% XGBoost) for final diagnostic confidence.
- Python 3.10+ — Download Python
- pip — Comes bundled with Python 3.4+
- Git — Download Git
- A virtual environment tool (recommended: built-in
venv)
Follow these steps to get MelanoSense running on your local machine:
1. Clone the repository
git clone https://github.com/prayangshuuu/MelanoSense.git
cd MelanoSense2. Create and activate a virtual environment
This isolates project dependencies from your system Python.
# Create the virtual environment
python -m venv .venv
# Activate it
# macOS / Linux:
source .venv/bin/activate
# Windows (Command Prompt):
.venv\Scripts\activate
# Windows (PowerShell):
.venv\Scripts\Activate.ps1You should see
(.venv)at the beginning of your terminal prompt when the environment is active.
3. Install all dependencies
All required packages are listed in requirements.txt. Install them in one go:
pip install -r requirements.txtWhat gets installed?
| Package | Purpose |
|---|---|
django |
Web framework powering the application |
tensorflow |
Deep learning engine for the CNN model |
xgboost |
Gradient boosting for metadata classification |
joblib |
Loading pre-trained label encoders |
pillow |
Image file handling and preprocessing |
numpy |
Numerical operations for model inference |
opencv-python |
Grad-CAM heatmap generation and image processing |
xhtml2pdf |
Rendering HTML templates into downloadable PDF reports |
4. Apply database migrations
This sets up the SQLite database schema required by the application:
python manage.py migrate5. Create a superuser (optional)
If you want access to the Django admin panel:
python manage.py createsuperuser6. Start the development server
python manage.py runserverThe application will be available at http://127.0.0.1:8000/
MelanoSense/
├── MelanoSense/ # Django project settings & configuration
│ ├── settings.py
│ ├── urls.py
│ └── wsgi.py
├── predictor/ # Core application — models, views, AI logic
│ ├── models.py # Database models (MedicalImage, Scan)
│ ├── views.py # Request handlers & prediction workflow
│ ├── utils.py # Hybrid inference engine, Grad-CAM, risk metadata
│ ├── forms.py # Patient data input forms
│ └── urls.py # App-level URL routing
├── ml_models/ # Pre-trained model files
│ ├── cnn_model.h5 # TensorFlow CNN weights
│ ├── xgboost_metadata_model.json
│ └── meta_label_encoders.pkl
├── templates/ # Django HTML templates
├── static/ # CSS, JavaScript, and static assets
├── media/ # User-uploaded images (created at runtime)
└── requirements.txt # Python dependencies
Built by Team Minus One
Distributed under the MIT License. See LICENSE for more information.