Infectious Disease

Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images.

Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to ...

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets ...

A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.

BACKGROUND: Most of existing deep learning research in medical image analysis is focused on networks with stronger performance. These networks have ac...

Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.

Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would app...

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and ...

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a v...

A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification.

Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria pa...

PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy.

Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification ...

Deep-AVPpred: Artificial Intelligence Driven Discovery of Peptide Drugs for Viral Infections.

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic vir...

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalent...

Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.

Chest X-ray (CXR) images are considered useful to monitor and investigate a variety of pulmonary disorders such as COVID-19, Pneumonia, and Tuberculos...

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost.

The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Co...

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.

Tuberculosis (TB) is an airborne disease caused by . It is imperative to detect cases of TB as early as possible because if left untreated, there is a...

Development of a deep learning-based quantitative structure-activity relationship model to identify potential inhibitors against the 3C-like protease of SARS-CoV-2.

In the recent COVID-19 pandemic, SARS-CoV-2 infection spread worldwide. The 3C-like protease (3CLpro) is a promising drug target for SARS-CoV-2. We ...

Dynamic feature learning for COVID-19 segmentation and classification.

Since December 2019, coronavirus SARS-CoV-2 (COVID-19) has rapidly developed into a global epidemic, with millions of patients affected worldwide. As ...

A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting.

The coronavirus disease 2019 (COVID-19) has spread rapidly around the world since 2020, with a significant fatality rate. Until recently, numerous cou...

T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis.

OBJECTIVES: This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) ...

COVID-19 and public support for autonomous technologies-Did the pandemic catalyze a world of robots?

By introducing a novel risk to human interaction, COVID-19 may have galvanized interest in uses of artificial intelligence (AI). But was the pandemic ...

Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications.

In the modern world, Tuberculosis (TB) is regarded as a serious health issue with a high rate of mortality. TB can be cured completely by early diagno...

Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant.