COVID-19

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network.

BACKGROUND: COVID-19 diagnosis in symptomatic patients is an important factor for arranging the nece...

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images.

Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the...

Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study.

In clinical routine, wound documentation is one of the most important contributing factors to treati...

A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network.

As a result of long-term pressure from train operations and direct exposure to the natural environme...

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly d...

A robot goes to rehab: a novel gamified system for long-term stroke rehabilitation using a socially assistive robot-methodology and usability testing.

BACKGROUND: Socially assistive robots (SARs) have been proposed as a tool to help individuals who ha...

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images.

Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in peo...

Country transition index based on hierarchical clustering to predict next COVID-19 waves.

COVID-19 has widely spread around the world, impacting the health systems of several countries in ad...

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly ...

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, espe...

A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data.

Most common diseases are influenced by multiple gene interactions and interactions with the environ...

Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the ava...

Understanding terror states of online users in the context of COVID-19: An application of Terror Management Theory.

The COVID-19 pandemic has provided psych challenges for many in society. One such challenge is the a...

Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays.

INTRODUCTION: The objective of this study was to assess seven configurations of six convolutional de...

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective...

Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling.

The significant health and economic effects of COVID-19 emphasize the requirement for reliable forec...

Improve automatic detection of animal call sequences with temporal context.

Many animals rely on long-form communication, in the form of songs, for vital functions such as mate...

The Infectious Disease Ontology in the age of COVID-19.

BACKGROUND: Effective response to public health emergencies, such as we are now experiencing with CO...

Identifying mislabelled samples: Machine learning models exceed human performance.

BACKGROUND: It is difficult for clinical laboratories to identify samples that are labelled with the...

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms...

Benchmarking and Testing Machine Learning Approaches with BARRA:CuRDa, a for Cancer Research.