retinal-imaging

Automated segmentation, phenotyping, and spatial analysis of retinal flat-mount images with reproducible study workflows and publication-ready outputs.

Command-line tool for finding, inspecting, citing, and accessing 350+ ophthalmology datasets from official sources.

AI-powered diabetic retinopathy detection using ResNet-152 deep learning. Web app with Flask REST API, PyTorch, real-time retinal image classification, 5-stage severity detection. Medical imaging, computer vision, transfer learning, healthcare AI, ophthalmology screening tool.

Multimodal retinal imaging biomarker explorer with fundus/OCT/tabular fusion, explainability, missing-modality support, and Streamlit dashboard

AI for retinal health to classify AMD, assess fundus image quality, and generate synthetic retina images with PyTorch.

Deep learning–based Glaucoma detection using CNN, Transfer Learning, and NSGA-II optimization, with a Flask web app for real-time predictions and Grad-CAM visualizations.

Deep learning system for non-invasive cardiovascular risk prediction using retinal fundus images. Hybrid EfficientNet-B3 + ViT with clinical data fusion.

Fundusnap mobile app — accessible, affordable diabetic retinopathy screening using a smartphone lens and cloud AI to combat preventable blindness in underserved areas (Flutter).

FundusNet: A Deep-Learning Approach for Fast Diagnosis of Neurodegenerative and Eye Diseases Using Fundus Images.

Web app for age-related macular degeneration (AMD) detection from retinal fundus images, with image quality assessment and Grad-CAM visual explanations.

Computational Stability of Cubical Homology: Orthogonal Controls for Fault Isolation in Retinal Diagnostics

We will cure Diabetic Retinopathy

Detection and classification of diabetic retinopathy stages from retinal fundus images using GLCM texture feature extraction, Student's T-test feature selection, and SVM — 91.1% test accuracy, 95% AUC

An AI-driven healthcare platform for cardiovascular disease (CVD) risk assessment using patient clinical biometrics and retinal eye image analysis. Built with Scikit-Learn, XGBoost, and Python to perform non-invasive early risk stratification and feature correlation.

Quantitative photometric and radiometric framework for simulating retinal photopigment bleaching and optoretinographic stimulus design.

takes Heidelberg DICOM export files and opens a heyex style viewer allowing for bscan to enface marking

AI in Medicine projects focused on medical imaging, retinal analysis, radiomics, machine learning, and deep learning

AI-powered diabetic retinopathy screening system using Python, Streamlit, and machine learning for retinal image analysis.

Retinal blood-vessel segmentation on the DRIVE dataset: a 2-channel (CLAHE + Frangi) U-Net in PyTorch with a FastAPI overlay service. F1 0.833 (inside FOV).

API service for the Fundusnap app — detects signs of diabetic retinopathy from fundus images using Azure Custom Vision, object-detection AI, and an LLM medical chat (Bun + Express + MongoDB).