Diabetic Retinopathy Detection From Retinal Images

Diabetic Retinopathy Detection From Retinal Images

Abstract:

Diabetic retinopathy can blind you. Retinal blood vessel extraction affects the light-sensitive retina at the back of the eye. Diabetes is the leading cause of vision loss and blindness in working-age adults. We propose extracting retinal blood vessels to detect eye diseases.

Introduction:

Diabetic retinopathy can cause blindness and vision loss. Retinal blood vessel damage causes vision loss. Early detection and treatment of diabetic retinopathy are essential. Manually extracting retinal blood vessels takes time. Thus, we aim to create an automated algorithm that simplifies extraction and improves detection.

Objectives:

Our project extracts retinal blood vessels from retinal images to detect diabetic retinopathy. We use automated methods to simplify and improve eye disease diagnosis and monitoring.

Project Details:

Our system extracts retinal blood vessels and detects diabetic retinopathy using multiple methods. Filtering removes environmental noise from retinal images. Local entropy thresholding and alternative sequential filter methods improve vessel extraction accuracy. MATLAB, a popular image processing language, implemented the system.

The system pre-processes retinal images to improve accuracy. Remove unwanted objects from the image and use algorithms to extract retinal blood vessels. Filtering improves blood vessel visibility and diabetic retinopathy detection.

Conclusion:

Our system automatically detects diabetic retinopathy by extracting retinal blood vessels from retinal images. Filtering and advanced algorithms improve detection accuracy and efficiency. This system may help doctors diagnose eye diseases early, improving treatment outcomes and reducing the risk of vision loss and blindness in diabetics.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.