AIMC Topic: Fundus Oculi

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Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.

The British journal of ophthalmology
BACKGROUND: The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated methods to determine visual acuity (VA) may help addr...

Multi-task deep learning for glaucoma detection from color fundus images.

Scientific reports
Glaucoma is an eye condition that leads to loss of vision and blindness if not diagnosed in time. Diagnosis requires human experts to estimate in a limited time subtle changes in the shape of the optic disc from retinal fundus images. Deep learning m...

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...

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN.

Sensors (Basel, Switzerland)
Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolut...

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs.

BioMed research international
Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings publi...

Optimized convolution neural network based multiple eye disease detection.

Computers in biology and medicine
World health organization (WHO) reports around 2.2 billion people in the world as visually challenged which is mostly due to the age-related eye diseases such as age-related macular degeneration (AMD), cataract, diabetic retinopathy (DR) and glaucoma...

A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique.

Medical & biological engineering & computing
Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medic...

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...

LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing.

Sensors (Basel, Switzerland)
Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the d...

Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network.

Eye (London, England)
OBJECTIVES: To develop and validate an end-to-end region-based deep convolutional neural network (R-DCNN) to jointly segment the optic disc (OD) and optic cup (OC) in retinal fundus images for precise cup-to-disc ratio (CDR) measurement and glaucoma ...