Ophthalmology

Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs.

Retinal related diseases are the leading cause of vision loss, and severe retinal lesion causes irreversible damage to vision. Therefore, the automatic methods for retinal diseases detection based on medical images is essential for timely treatment. Considering that manual diagnosis and analysis of medical images require a large number of qualified experts, deep learning can effectively diagnosis ...

Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases.

Farming is essential to the long-term viability of any economy. It differs in each country, but it is essential for long-term economic success. Only a few of the agricultural industry's issues include a lack of suitable irrigation systems, weeds, and plant monitoring concerns as a consequence of efficient management in distinct open and closed zones for crop and plant treatment. The objective of t...

Self-relabeling for noise-tolerant retina vessel segmentation through label reliability estimation.

BACKGROUND: Retinal vessel segmentation benefits significantly from deep learning. Its performance relies on sufficient training images with accurate ...

Deep Learning Approach for Automatic Microaneurysms Detection.

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shap...

Revealing Fine Structures of the Retinal Receptive Field by Deep-Learning Networks.

Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the v...

Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: a cross-sectional study.

Central serous chorioretinopathy (CSC) is the fourth most common retinopathy and can reduce quality of life. CSC is assessed using optical coherence t...

Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images.

Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exu...

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization.

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Wherea...

Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study.

PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus ph...

Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.

Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in ...

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment ...

[Artificial intelligence and glaucoma: A literature review].

In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma...

Plasmonic Optoelectronic Memristor Enabling Fully Light-Modulated Synaptic Plasticity for Neuromorphic Vision.

Exploration of optoelectronic memristors with the capability to combine sensing and processing functions is required to promote development of efficie...

Integrated In-Sensor Computing Optoelectronic Device for Environment-Adaptable Artificial Retina Perception Application.

With the development and application of artificial intelligence, there is an appeal to the exploitation of various sensors and memories. As the most i...

[Ocular changes as a diagnostic tool for malaria].

BACKGROUND: According to the WHO Malaria Report 2019 a total of 229 million people fall ill with malaria each year and two thirds of deaths involve ch...

A Case for the Use of Artificial Intelligence in Glaucoma Assessment.

We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect g...

Visual-Textual Hybrid Sequence Matching for Joint Reasoning.

Reasoning is one of the central topics in artificial intelligence. As an important reasoning paradigm, entailment recognition has attracted much resea...

Hierarchical Deep Embedding for Aurora Image Retrieval.

Retrieving informative images from the large-scale aurora data is of great significance in the field of space physics. In this article, we propose a h...

The Contribution of Artificial Intelligence in Achieving the Sustainable Development Goals (SDGs): What Can Eye Health Can Learn From Commercial Industry and Early Lessons From the Application of Machine Learning in Eye Health Programmes.

Achieving The United Nations sustainable developments goals by 2030 will be a challenge. Researchers around the world are working toward this aim acro...

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.

PURPOSE: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indic...