Detecting Diabetic Retinopathy in Retina Photos Blindness with Deep Learning using CNNs

In this video, we present Detecting Diabetic Retinopathy in Retinal Photos using Deep Learning with Convolutional Neural Networks (CNNs), focusing on how AI can assist in early diagnosis to prevent vision loss and blindness.

This session covers:
• Introduction to Diabetic Retinopathy (DR) and its impact on vision
• Importance of early detection in preventing blindness
• Overview of Deep Learning and CNNs in medical imaging
• Retinal image dataset and pre-processing techniques
• CNN architecture and training process
• Model evaluation using metrics like accuracy, sensitivity, specificity, and AUC
• Real-world applications in clinical decision support systems

This system helps in:
• Automatic and accurate screening of diabetic retinopathy
• Reducing workload of ophthalmologists
• Enabling early intervention and treatment
• Improving accessibility of eye care in remote areas

This video is ideal for students and professionals in Artificial Intelligence, Deep Learning, Computer Vision, Biomedical Engineering, and Healthcare Analytics.
📌 Suitable for:
CSE / AI / Data Science students
Medical imaging projects
Healthcare AI research
Final year projects and seminars

#DiabeticRetinopathy #DeepLearning #CNN #MedicalImaging #HealthcareAI #ComputerVision #BlindnessPrevention #FinalYearProject #ResearchProject #AIinHealthcare

Видео Detecting Diabetic Retinopathy in Retina Photos Blindness with Deep Learning using CNNs канала Jack Sparrow Publishers