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).

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

Diabetic retinopathy severity classifier — ResNet34 fine-tuned for 5-class fundus grading (0.815 self-reported val accuracy). fastai training notebook + ONNX weights.

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).

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

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.

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

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

Systematic safety audit of a ResNet-50 diabetic retinopathy classifier using the Medical Algorithmic Audit framework (Liu et al., 2022). Included error analysis, subgroup testing, adversarial robustness, and FMEA risk scoring on the APTOS 2019 dataset.

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).

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.