An AI-based diagnostic tool that detects and grades diabetic retinopathy from retinal images.
A CNN/U-Net model segments microaneurysms, exudates and hemorrhages from retinal images after contrast enhancement and noise removal, then grades disease severity as mild, moderate or severe — evaluated on accuracy, Dice score and IoU and served through a real-time web interface.
Diabetic retinopathy is a leading cause of preventable blindness, and screening depends on scarce ophthalmologist time. Classification alone is not enough — clinicians need to see which lesions drove the grade.
Segment the individual lesion types rather than classifying the image as a whole, so the severity grade is backed by visible, localised evidence.
Lesion-Based Segmentation
CNN/U-Net model segments microaneurysms, exudates, and hemorrhages.
Image Preprocessing
Contrast enhancement and noise removal for cleaner input images.
Severity Classification
Grades disease as mild, moderate, or severe.
Real-Time Web Prediction
Evaluated via accuracy, Dice score, and IoU metrics.
Deep Learning
Image processing
Evaluation
Application
Retinal image input
A fundus image is uploaded to the system.
Preprocessing
Contrast is enhanced and noise removed.
Lesion segmentation
U-Net segments each lesion type pixel-wise.
Grading
Severity is classified from the segmented lesions.
Result display
Overlays and grade are returned in real time.
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Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.
That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.
We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.
Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.
Get complete source code, documentation, PPT, explanation, and support.
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