Glaucoma Detection Using Deep Learning | IEEE Final Year Project with Code, Docs & Mentorship
👁️ Glaucoma Detection Using Deep Learning | IEEE-Based Final Year Project
In this cutting-edge deep learning project, we developed an intelligent system to detect glaucoma from retinal fundus images. Glaucoma is one of the leading causes of irreversible blindness, and early detection is crucial. Our model classifies fundus images as glaucomatous or healthy, providing a cost-effective and scalable solution for early diagnosis.
📚 Built using insights from multiple IEEE research papers, the project includes:
Image preprocessing (CLAHE, denoising, optic disc localization)
CNN models like EfficientNet, ResNet50, and Hybrid CNNs
Real-time prediction via a web application
Feature visualization for clinical explainability
🧠 Ideal For:
Final Year Engineering/MSc/MCA/BSc Students
Medical imaging researchers
AI solutions in ophthalmology
Deep Learning enthusiasts in healthcare
💻 Tech Stack Includes:
Python, TensorFlow/Keras
OpenCV for fundus image processing
EfficientNet, ResNet50, VGG16-based CNNs
Flask (Backend), HTML/CSS/JS (Frontend)
📦 What You’ll Get:
✅ Complete source code with dataset
✅ IEEE-style documentation (report, synopsis, PPT)
✅ Mentorship and video-based project explanation
✅ Deployment-ready web application with UI
📩 Contact for code, customization, and support:
📱 +91-8088605682
🌐 Website: https://smartaitechnologies.com/
🏢 About Smart AI Technologies
We offer IEEE final year projects and complete support in:
Medical AI (Glaucoma, Pneumonia, Breast Cancer, etc.)
Image classification & deep learning systems
IoT, NLP, ML, Cybersecurity, and custom research projects
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🎥 Explore all project demos on YouTube: Smart AI Technologies
🔥 Hashtags:
#GlaucomaDetection #AIInHealthcare #DeepLearningProject #IEEEFinalYearProject #OphthalmologyAI #MedicalAI #SmartAITechnologies #PythonAIProject #CNNModel #FundusImageAnalysis