This project introduces a non-invasive approach to blood group prediction using fingerprint image processing and machine learning. By leveraging Convolutional Neural Networks (CNNs), it classifies fingerprint patterns into eight common blood groups (A+, A-, B+, B-, AB+, AB-, O+, O-), offering a quick and accessible alternative to traditional methods.
✅ Rapid Blood Group Identification – Provides a fast and accurate alternative to traditional methods.
✅ Accessibility in Remote Areas – Enables blood group prediction without lab facilities or skilled personnel.
✅ Integration with Portable Devices – Supports point-of-care diagnostics in clinics and mobile units.
✅ Safety and Scalability – Reduces contamination risks and ensures adaptability across healthcare settings.
✅ Biometric and Medical Synergy – Combines biometrics and machine learning for improved diagnostics.
- HTML
- CSS
- JavaScript
- Flask
- SQLAlchemy
- SQLite
- TensorFlow / Keras
- Google Colab
| 🧠 Model | 🎯 Testing Accuracy | 📊 Validation Accuracy |
|---|---|---|
| VGG16 | ✅ 88.72% | ✅ 89.50% |
| AlexNet | 🔴 12.47% | 🔴 12.49% |
| ResNet50 | 🟡 61.19% | 🟡 62.70% |
| Hybrid Model (EfficientNetB0 + SVM) | 🔵 22.29% | 🔵 22.81% |
- 📊 Expand the dataset for better generalization.
- 🧪 Experiment with advanced models to improve accuracy.
- 🌐 Deploy the model in a live environment for real-world use.
📧 [email protected]
🔗 LinkedIn
📧 [email protected]
🔗 LinkedIn
⭐️ Feel free to contribute and star the repository if you find it helpful!