Code repository for a paper "Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network"

Actively maintained and comprehensive public glaucoma dataset catalog

Optic Disc and Optic Cup Segmentation using 57 layered deep convolutional neural network

AUTOMATED TYPE CLASSIFICATION OF GLAUCOMA DETECTION USING DEEP LEARNING

An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public full-fundus glaucoma images and associated metadata.

Source code for GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images

Glaucoma detection using deep learning(cnn)

Evaluation of a simple CNN model for glaucoma detection trained on a single public dataset against complex architectures trained on multiple public/private datasets

Glaucoma Detection based on Optic Cup and Disc Segmentation using U-Net

This project uses deep learning algorithms and the Keras library to determine if a person has certain diseases or not from their chest x-rays and other scans. The trained model is displayed using Streamlit, which enables the user to upload an image and receive instant feedback.

Deep ConvNets based eye cancer detection

A retinal knowledge-oriented dynamic multi-level feature integration glaucoma screening method based on fundus images, mitigating the existing approaches in capturing subtle pathological clues outside the disc and the pain of weak generalizability,, which has reached the international leading diagnostic level on multiple public datasets.

Optic Disc Segmentation & Glaucoma Detection, PyTorch Version

Implementation of famous Optic disc and cup segmentation research papers in python.

Glaucoma and Non-Glaucoma classification using ML/Dl and ensemble approaches using Image Feature Extraction Using HOG (Histogram of Gradient)

A Transfer Learning Based Web App for Glaucoma Detection Using Low-Cost Ophthalmoscopic Camera

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.

AI-Powered Eye Disease Detection Web App An intelligent retina image classification system built using deep learning (VGG16), TensorFlow, and Flask. This open-source project helps detect common eye diseases like Cataract, Diabetic Retinopathy, and Glaucoma, and also identifies uncertain cases as Unknown.