Here we propose a CNN approach to diagnosing Glaucoma from fundus images and accurately classifying its severity.

aims to develop Deep Learning (DL) for prediction and classification of eye diseases by applying Gabor Filter as feature extractor as it can extract the important features such as textual and …

We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from …

There are some existing methodologies SVM, KNN, and Random Forest using text datasets and with a low accuracy rate. In this project, we apply deep learning models that can recognize the …

Sep 15, 2024 · The work of [46] explores various deep transfer learning models, including Basic CNN, Deep CNN, AlexNet 2, Xception, Inception V3, ResNet 50, and DenseNet121, for …

Sep 13, 2024 · AUTOMATED TYPE CLASSIFICATION OF GLAUCOMA DETECTION USING DEEP LEARNING. An adaptive threshold based algorithm for optic disc and cup segmentation …

Dec 8, 2021 · This study proposes an automatic method for detecting and classifying cataracts in their earliest stages by combining a deep learning (DL) model with the 2D-discrete Fourier …

In this paper, we suggest a powerful and accurate algorithm using a convolutional neural network (CNN) for the automatic diagnosis of glaucoma. In this work, 1113 fundus images consisting of …

Abstract: The increasing prevalence of retinal diseases such as Cataract, Diabetic Retinopathy, and Glaucoma necessitates accurate and efficient diagnostic tools for early detection and …

Major ONH methods use specific criteria like Vertical Cup to Disc Ratio (CDR), Rim to Disc Area Ratio (RDR), Disc Diameter and NRR for the detection of Glaucoma. Methods based on CDR …