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 imaginary patterns of the structure and shape of the images and then use its output as an input to …

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 the retinal images that are often overlooked by clinicians.

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 complex features needed for classification tasks, including …

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 predicting multiple eye diseases like diabetic macular edema, choroidal neovascularization, and glaucoma using fundus digital photography and OCT images. While this approach leverages ...

Sep 13, 2024 · 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.

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 transform (DFT...

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 660 normal and 453 glaucomatous images from four databases have been used for the diagnosis 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 treatment. This study employs a deep learning-based approach using EfficientNet-B3 for multi-class classification of retinal images.

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 are largely used when a higher CDR shows greater risk in Glaucoma. Fundoscopy is prominent Biomedical imaging techniques to analyze the retina.