AIMC Topic: Retina

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Single-shot retinal image enhancement using untrained and pretrained neural networks priors integrated with analytical image priors.

Computers in biology and medicine
Retinal images acquired using fundus cameras are often visually blurred due to imperfect imaging conditions, refractive medium turbidity, and motion blur. In addition, ocular diseases such as the presence of cataracts also result in blurred retinal i...

Automated image curation in diabetic retinopathy screening using deep learning.

Scientific reports
Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output ...

Multi-class retinal fluid joint segmentation based on cascaded convolutional neural networks.

Physics in medicine and biology
. Retinal fluid mainly includes intra-retinal fluid (IRF), sub-retinal fluid (SRF) and pigment epithelial detachment (PED), whose accurate segmentation in optical coherence tomography (OCT) image is of great importance to the diagnosis and treatment ...

Automatic segmentation of multitype retinal fluid from optical coherence tomography images using semisupervised deep learning network.

The British journal of ophthalmology
BACKGROUND/AIMS: To develop and validate a deep learning model for automated segmentation of multitype retinal fluid using optical coherence tomography (OCT) images.

Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model.

Computational intelligence and neuroscience
In today's world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may ...

A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images.

Scientific reports
Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a ...

A Detailed Systematic Review on Retinal Image Segmentation Methods.

Journal of digital imaging
The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent fur...

Towards more efficient ophthalmic disease classification and lesion location via convolution transformer.

Computer methods and programs in biomedicine
OBJECTIVE: A retina optical coherence tomography (OCT) image differs from a traditional image due to its significant speckle noise, irregularity, and inconspicuous features. A conventional deep learning architecture cannot effectively improve the cla...

Artificial intelligence based detection of age-related macular degeneration using optical coherence tomography with unique image preprocessing.

European journal of ophthalmology
PURPOSE: The aim of the study is to improve the accuracy of age related macular degeneration (AMD) disease in its earlier phases with proposed Capsule Network (CapsNet) architecture trained on speckle noise reduced spectral domain optical coherence t...

Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.

Sensors (Basel, Switzerland)
With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a p...