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Optic cup segmentation of stereo retinal fundus images using virtual reality
Glaucoma is one of the world leading causes of irreversible blindness. Early detection is essential to delay its progression and prevent vision loss....
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RLeU-Net: Segmentation of blood vessels in retinal fundus images for Diabetic Retinopathy Screening
Diabetic Retinopathy (DR) is a primitive cause of blindness in diabetic patients. There are chances for DR in certain cases, due to the damage in the...
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Pathologic myopia diagnosis and localization from retinal fundus images using custom CNN
Pathologic myopia (PM) is the critical factor of irreversible visual artifacts and puts patients at risk of other severe retinal diseases such as...
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Disease localization and its prediction from retinal fundus images using explicitly designed deep learning architecture
Visual disability is increasing due to the incidence of diabetic retinopathy (DR), but timely detection and diagnosis can provide more treatment...
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CA-GAN: A Method to Narrow Down Domain Differences of Retinal Fundus Images Caused by Camera Brands
There is a significant body of research that focuses on utilizing deep learning to automatically detect diabetic retinopathy (DR). However, applying...
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Enhancing rare retinal disease classification: a few-shot meta-learning framework utilizing fundus images
Screening techniques based on fundus imaging are very popular in diagnosing retinal diseases. In the field of fundus image classification, deep...
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A novel Adaptive Neural Network-Based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images
Diabetic retinopathy (DR) is a human eye disease in which the eye’s retina is damaged in diabetics. Diabetic retinopathy can be diagnosed by manually...
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A novel hybridized feature selection strategy for the effective prediction of glaucoma in retinal fundus images
Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional data. The FS approach selects influential traits and...
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Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification
Glaucoma is a prevalent cause of blindness worldwide. If not treated promptly, it can cause vision and quality of life to deteriorate. According to...
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Multi-dimensional cascades neural network models for the segmentation of retinal vessels in colour fundus images
Deep learning has recently received attention as one of the most popular methods for boosting performance in different sectors, including medical...
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A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images
Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the...
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Application of deep learning approaches for classification of diabetic retinopathy stages from fundus retinal images: a survey
Diabetic retinopathy (DR) is an impediment of diabetes mellitus, which if not treated early may result in complete loss of vision, even without any...
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Diabetic retinopathy detection and grading of retinal fundus images using coyote optimization algorithm with deep learning
Diabetic retinopathy (DR) is a major reason of preventable blindness for diabetic patients. Regular retinal screening is recommended for diabetic...
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Glaucoma Detection Using Clustering and Segmentation of the Optic Disc Region from Retinal Fundus Images
Segmentation is a process of dividing image into multiple parts. Each part is called a segment. The main objective of the segmenting image is to...
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Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images
The segmentation of blood vessels through color fundus images is a difficult and time-consuming task that requires experienced clinicians. Recently,...
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Noise reduction deep CNN-based retinal fundus image enhancement using recursive histogram
Retinal imaging often falls short in image quality due to limitations in imaging conditions. Issues such as low contrast and inadequate brightness...
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Retinal Age Estimation with Temporal Fundus Images Enhanced Progressive Label Distribution Learning
Retinal age has recently emerged as a reliable ageing biomarker for assessing risks of ageing-related diseases. Several studies propose to train deep...
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MDP-HML: an efficient detection method for multiple human disease using retinal fundus images based on hybrid learning techniques
Recently, medical image processing has improved the quality of medical images for disease prediction in humans. For multiple disease prediction...
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An efficient multitasking cascade network for arteriovenous segmentation using dual-modal fundus images
Our eyesight does not remain the same throughout our lives. Certain diseases start affecting our vision with age, such as diabetic retinopathy,...
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Res-Unet based blood vessel segmentation and cardio vascular disease prediction using chronological chef-based optimization algorithm based deep residual network from retinal fundus images
Cardiovascular disease (CVD) is a significant contributor to global mortality in our advanced society. The majority of males were attributed to...
