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

AUTOMATED TYPE CLASSIFICATION OF GLAUCOMA DETECTION USING DEEP LEARNING

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

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

Actively maintained and comprehensive public glaucoma dataset catalog

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

Deep ConvNets based eye cancer detection

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 using deep learning(cnn)

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

Optic Disc Segmentation & Glaucoma Detection, PyTorch Version

Deep learning project for ocular eye disease classification

Glaucoma detection transfer learning model designed with EfficientNet

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

Automated diagnosis of glaucoma using machine learning

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.

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

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

In this paper, we developed a machine learning model ensemble approach consisting of a support vector machine (SVM), random forest (RF), Multilayer Perceptron (MLP), and Majority-VotingEnsemble classifiers.