Infectious Disease

Integration of CNN, CBMIR, and Visualization Techniques for Diagnosis and Quantification of Covid-19 Disease.

Diagnosis techniques based on medical image modalities have higher sensitivities compared to conventional RT-PCT tests. We propose two methods for diagnosing COVID-19 disease using X-ray images and differentiating it from viral pneumonia. The diagnosis section is based on deep neural networks, and the discriminating uses an image retrieval approach. Both units were trained by healthy, pneumonia, a...

Lung Lesion Localization of COVID-19 From Chest CT Image: A Novel Weakly Supervised Learning Method.

Chest computed tomography (CT) image data is necessary for early diagnosis, treatment, and prognosis of Coronavirus Disease 2019 (COVID-19). Artificial intelligence has been tried to help clinicians in improving the diagnostic accuracy and working efficiency of CT. Whereas, existing supervised approaches on CT image of COVID-19 pneumonia require voxel-based annotations for training, which take a l...

Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data.

In the present epidemic of the coronavirus disease 2019 (COVID-19), radiological imaging modalities, such as X-ray and computed tomography (CT), have ...

COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images.

The COVID-19 pandemic has a significant negative effect on people's health, as well as on the world's economy. Polymerase chain reaction (PCR) is one ...

Identifying genomic islands with deep neural networks.

BACKGROUND: Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources includin...

Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach.

First total synthesis of chromanone A, preparation of related compounds and evaluation of their antifungal activity against , a biofilm forming agent.

A straightforward and convenient approach for the first total syntheses of chromanone A and a related 7-OMe substituted natural product is reported. T...

Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.

Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect ...

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.

BACKGROUND: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discr...

Automated detection of Mycobacterium tuberculosis using transfer learning.

INTRODUCTION: Quantitative analysis of Mycobacterium tuberculosis using microscope is very critical for diagnosing tuberculosis diseases. Microbiologi...

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Schistosomiasis is a parasitic disease caused by trematode worms of the genus and affects over 200 million people worldwide. The control and treatmen...

The use of a UV-C disinfection robot in the routine cleaning process: a field study in an Academic hospital.

BACKGROUND: Environmental surface decontamination is a crucial tool to prevent the spread of infections in hospitals. However, manual cleaning and dis...

Immunomodulatory and antimicrobial non-mulberry silk fibroin accelerates fibroblast repair and regeneration by protecting oxidative stress.

The antimicrobial nature of silk-fibroin (SF) is reported but antioxidant potential and the immunomodulatory role towards the fibroblast cell repair ...

A deep learning algorithm for 3D cell detection in whole mouse brain image datasets.

Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy ...

The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent.

In the field of machine learning, building models and measuring their performance are two equally important tasks. Currently, measures of precision of...

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases.

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant...

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.

BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19.

Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease.

We report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID...

Swarm Learning for decentralized and confidential clinical machine learning.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be ...

Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19.

Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with mini...