Pneumonia

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the c...

Three-Terminal Ovonic Threshold Switch (3T-OTS) with Tunable Threshold Voltage for Versatile Artificial Sensory Neurons.

Inspired by information processing in biological systems, sensor-combined edge-computing systems attract attention requesting artificial sensory neurons as essential ingredients. Here, we introduce a simple and versatile structure of artificial sensory neurons based on a novel three-terminal Ovonic threshold switch (3T-OTS), which features an electrically controllable threshold voltage (). Combine...

Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?

PURPOSE: Over the last 2 years, the artificial intelligence (AI) community has presented several automatic screening tools for coronavirus disease 201...

Deep learning prediction of sex on chest radiographs: a potential contributor to biased algorithms.

BACKGROUND: Deep convolutional neural networks (DCNNs) for diagnosis of disease on chest radiographs (CXR) have been shown to be biased against males ...

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model.

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive c...

A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images.

. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people...

CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection.

The workload of radiologists has dramatically increased in the context of the COVID-19 pandemic, causing misdiagnosis and missed diagnosis of diseases...

Attention based automated radiology report generation using CNN and LSTM.

The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. ...

Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data.

Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of C...

AMMU: A survey of transformer-based biomedical pretrained language models.

Transformer-based pretrained language models (PLMs) have started a new era in modern natural language processing (NLP). These models combine the power...

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play ma...

Tiled Sparse Coding in Eigenspaces for Image Classification.

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful...

BEMD-3DCNN-based method for COVID-19 detection.

The coronavirus outbreak continues to spread around the world and no one knows when it will stop. Therefore, from the first day of the identification ...

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images.

The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional ...

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...

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The gr...

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

OBJECTIVES: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model ...

Classification of COVID-19 and Pneumonia Using Deep Transfer Learning.

The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identif...

D2-CovidNet: A Deep Learning Model for COVID-19 Detection in Chest X-Ray Images.

Since the outbreak of Coronavirus disease 2019 (COVID-19), it has been spreading rapidly worldwide and has not yet been effectively controlled. Many r...

Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia.

This work aimed to explore the application value of deep learning-based magnetic resonance imaging (MRI) images in the identification of tuberculosis ...