COVID-19

TransMorph: Transformer for unsupervised medical image registration.

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently, Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-a...

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques.

: Since December 2019, the coronavirus (COVID-19) epidemic has sparked considerable alarm among the general community and significantly affected societal attitudes and perceptions. Apart from the disease itself, many people suffer from anxiety and depression due to the disease and the present threat of an outbreak. Due to the fast propagation of the virus and misleading/fake information, the issue...

Prescreening and Triage of COVID-19 Patients Through Chest X-Ray Images Using Deep Learning Model.

Deep learning models deliver a fast diagnosis during triage prescreening for COVID-19 patients, reducing waiting time for hospital admission during he...

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a ...

RCMNet: A deep learning model assists CAR-T therapy for leukemia.

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive thera...

Eliciting a User's Preferences by the Self-Disclosure of Socially Assistive Robots in Local Households of Older Adults to Facilitate Verbal Human-Robot Interaction.

To realize a society in which older adults can live independently in their homes and familiar environments for as long as possible, their lives can be...

Ultrasonic image denoising using machine learning in point contact excitation and detection method.

A point contact/Coulomb coupling technique is generally used for visualizing the ultrasonic waves in Lead Zirconate Titanate (PZT) ceramics. The point...

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches.

Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health c...

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches.

Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radio...

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.

Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification....

A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation.

Deep learning methods have enabled a fast, accurate and automated approach for retinal layer segmentation in posterior segment OCT images. Due to the ...

The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking.

Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of n...

An Efficient Deep Learning Mechanism for the Recognition of Olive Trees in Jouf Region.

Olive trees grow all over the world in reasonably moderate and dry climates, making them fortunate and medicinal. Pesticides are required to improve c...

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.

Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Bec...

BRefine: Achieving High-Quality Instance Segmentation.

Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptiona...

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

BACKGROUND: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical servic...

Ensemble of Deep Neural Networks based on Condorcet's Jury Theorem for screening Covid-19 and Pneumonia from radiograph images.

COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. Deep Neural Networks with hundr...

Study on Accuracy Improvement of Slope Failure Region Detection Using Mask R-CNN with Augmentation Method.

We proposed an automatic detection method of slope failure regions using a semantic segmentation method called Mask R-CNN based on a deep learning alg...

Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning.

Acyl-CoA synthetase long-chain family member 4 (ACSL4) has been linked to the occurrence of tumors and is implicated in the ferroptosis process. Deep ...

Comparative studies of deep learning segmentation models for left ventricle segmentation.

One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the lef...