Ophthalmology

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents.

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificia...

Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is ...

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

BACKGROUND: Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocula...

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.

PURPOSE: Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coheren...

Predictions of ocular changes caused by diabetes in glaucoma patients.

BACKGROUND AND OBJECTIVE: This paper builds different neural network models with simple topologies, having one or two hidden layers which were subsequ...

A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image.

Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven diffi...

OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications.

PURPOSE: Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications have become the standard of care for their respe...

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.

BACKGROUND AND OBJECTIVE: Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elic...

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image h...

Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration.

PURPOSE: To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular a...

Codebook-based electrooculography data analysis towards cognitive activity recognition.

With the advancement in mobile/wearable technology, people started to use a variety of sensing devices to track their daily activities as well as heal...

Learning ensemble classifiers for diabetic retinopathy assessment.

Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabeti...

A new hand-eye calibration approach for fracture reduction robot.

OBJECTIVE: The hand-eye calibration is used to determine the transformation between the end-effector and the camera marker of the robot. But the robot...

From biokinematics to a robotic active vision system.

Barn owls move their heads in very particular motions, compensating for the quasi-immovability of their eyes. These efficient predators often perform ...

Texture and art with deep neural networks.

Although the study of biological vision and computer vision attempt to understand powerful visual information processing from different angles, they h...

Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorl...

Air quality monitoring using mobile microscopy and machine learning.

Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. I...

Scaling up liquid state machines to predict over address events from dynamic vision sensors.

Short-term visual prediction is important both in biology and robotics. It allows us to anticipate upcoming states of the environment and therefore pl...

A lightweight, inexpensive robotic system for insect vision.

Designing hardware for miniaturized robotics which mimics the capabilities of flying insects is of interest, because they share similar constraints (i...

Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.

Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate...