COVID-19

Testing a global null hypothesis using ensemble machine learning methods.

Testing a global null hypothesis that there are no significant predictors for a binary outcome of interest among a large set of biomarker measurements is an important task in biomedical studies. We seek to improve the power of such testing methods by leveraging ensemble machine learning methods. Ensemble machine learning methods such as random forest, bagging, and adaptive boosting model the relat...

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a basel...

Robot-Assisted Prostate-Specific Membrane Antigen-Radioguided Surgery in Primary Diagnosed Prostate Cancer.

The objective of this study was to evaluate the safety and feasibility of Tc-based prostate-specific membrane antigen (PSMA) robot-assisted-radioguide...

INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network.

Testing is one of the important methodologies used by various countries in order to fight against COVID-19 infection. The infection is considered as o...

Biochemical algorithm to identify individuals with ALPL variants among subjects with persistent hypophosphatasaemia.

BACKGROUND: Hypophosphatasia (HPP) is a rare and underdiagnosed condition characterized by deficient bone and teeth mineralization. The aim of this st...

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics.

Anticipating intensive care unit (ICU) occupancy is critical in supporting decision makers to impose (or relax) measures that mitigate COVID-19 transm...

E-Learning Research Trends in Higher Education in Light of COVID-19: A Bibliometric Analysis.

This paper provides a broad bibliometric overview of the important conceptual advances that have been published during COVID-19 within "e-learning in ...

Deep learning representations to support COVID-19 diagnosis on CT slices.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic ana...

Classification of COVID-19 and Influenza Patients Using Deep Learning.

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, ...

COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.

Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-1...

Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.

The authors of this study developed the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with machine l...

AFD-StackGAN: Automatic Mask Generation Network for Face De-Occlusion Using StackGAN.

To address the problem of automatically detecting and removing the mask without user interaction, we present a GAN-based automatic approach for face d...

Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study.

Pulmonary medical image analysis using image processing and deep learning approaches has made remarkable achievements in the diagnosis, prognosis, and...

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System.

In today's scenario, many scientists and medical researchers have been involved in deep research for discovering the desired medicine to reduce the sp...

A Machine Learning Pipeline for Accurate COVID-19 Health Outcome Prediction using Longitudinal Electronic Health Records.

Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imagi...

Machine learning models for accurate prioritization of variants of uncertain significance.

The growing use of next-generation sequencing technologies on genetic diagnosis has produced an exponential increase in the number of variants of unce...

Deep reinforcement learning stock market trading, utilizing a CNN with candlestick images.

Billions of dollars are traded automatically in the stock market every day, including algorithms that use neural networks, but there are still questio...

Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning.

Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses eith...

The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19.

BACKGROUND: This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomic...

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of ext...