BloodEye: Blood Group Detection Using Eye Images
Abstract: BloodEye enables non-invasive detection of blood group from retinal/eye images via a convolutional neural network, providing rapid screening and risk assessment for transfusion compatibility and health monitoring.
ABSTRACT
BloodEye is a novel solution designed to predict individuals’ blood groups using retinal or ocular images, employing deep learning (CNN) algorithms and image-processing techniques. By analysing retina scans captured via standard fundus photography or eye-imaging devices, BloodEye produces accurate blood group classification, reduces dependency on invasive blood testing, and supports rapid health screening workflows.
EXISTING SYSTEM
- Traditional blood group detection requires a blood draw, lab reagents and manual serological testing.
- No image-based or non-invasive method widely available for blood group classification.
- Limited screening accessibility in mobile or remote-clinic settings.
These limitations lead to delays, logistical burdens, and restricted access to rapid blood typing for donor screening, transfusion preparation or emergency use.
OVERVIEW OF BLOODEYE
BloodEye integrates:
- 🧠 Convolutional Neural Network (CNN) for classifying blood groups (A, B, AB, O) from ocular images
- 📸 Retinal / Eye-Image Capture Module enabling fundus or retina photo input
- 📈 Result Dashboard showing classification probabilities and confidence scores
- ⚠️ Automated Alerts when image quality is insufficient or classification confidence is low
PROPOSED SYSTEM
BloodEye supports non-invasive blood group detection and quick screening for transfusion, donation and clinical workflows. Data inputs include high-resolution eye or retina images, patient metadata (age, gender), and optionally fundus image metadata. A trained CNN model analyses these images to output a predicted blood group along with probability/confidence metrics and recommends the next steps (e.g., confirm with lab test, donor match).
MODULES
- 📸 Image Capture & Pre-processing Module
- 🧠 CNN-Based Blood Group Prediction Engine
- 📊 Results Dashboard with blood-group classification probabilities and trend graphs
- 📧 Notification & Alert System (email/SMS for low confidence or poor image quality)
- 👩⚕️ Clinician Panel for review, verification and integration into donation / transfusion workflow
ADVANTAGES
- ✅ Non-invasive blood group detection via eye images
- ✅ Faster screening, reduced need for blood draw and reagents
- ✅ Portable and scalable for mobile clinics or remote settings
- ✅ Dashboard with confidence metrics helps in decision-support
- ✅ Enhances donor screening, transfusion prep and rapid health workflows
SOFTWARE & TECH STACK
- Backend: Python (Flask or Django)
- ML Frameworks: TensorFlow / Keras (CNN models)
- Image Processing: OpenCV, PIL
- Frontend: React.js or HTML/CSS/JS
- Database: MySQL or MongoDB
HARDWARE REQUIREMENTS
- Processor: Intel i3 / AMD Ryzen 3 or higher
- RAM: Minimum 4 GB (8 GB recommended for training CNNs)
- Storage: 100 GB (for image datasets and model checkpoints)
DEMO VIDEO
Watch the demo for an overview and walk-through:
YouTube: https://youtu.be/PQzQFiYHLZc?si=PqQrPWk-i4oHAVwX
INCLUDED PACKAGE
The package includes: Base Paper, Complete Source Code, Complete Documentation, Presentation Slides (PPT), Flow Diagram (UML), Database File, Screenshots, Execution Procedure, ReadMe File, Add-ons & Supporting Software, Video Tutorials.
SUPPORT & SPECIALIZATION
- Support via Ticketing System
- Voice Conference Assistance
- Video On Demand for Setup & Training
- Remote Connectivity Support
- Code Customization on Request
- Document Customization Assistance
- Live Chat Support
Contact: xpertieee@gmail.com
Explore more AI & ML healthcare projects at ieeexpert.com
CONCLUSION
BloodEye delivers a breakthrough non-invasive method for blood group detection via eye images, harnessing deep learning to speed up screening, enable remote deployment, and support clinical workflows.
Disclaimer: BloodEye is a decision-support tool, not a replacement for laboratory serology. Clinical validation and regulatory clearance are recommended before diagnostic use.
FAQ
How does BloodEye detect blood group from eye images?
It uses a CNN trained on large datasets of retinal/eye images labelled with known blood groups; image features are learned and mapped to classification outputs.Can this replace standard blood tests?
Not yet for definitive diagnosis—this is a screening and decision-support system. Confirmatory serology is still recommended.What types of images are required?
High-resolution fundus or retinal images captured via eye-imaging devices. Good lighting, focus and minimal artefact improve accuracy.
Published by: ieeexpert • Support: xpertieee@gmail.com
