COVID-19, a worldwide pandemic that has affected many people and thousands of
individuals have died due to COVID-19, during the last two years. Due to the benefits of …

The competence of machine learning approaches to carry out clinical expertise tasks has
recently gained a lot of attention, particularly in the field of medical-imaging examination …

This study presents an efficient four-stage ensemble deep learning framework for
diagnosing infectious diseases. The model is evaluated on three standard datasets. In our …

Nucleic acid testing is currently the golden reference for coronaviruses (SARS-CoV-2)
detection, while the SARS-CoV-2 antigen-detection rapid diagnostic tests (RDT) is an …

Purpose This study aimed to develop a deep learning model based on chest radiography
(CXR) images and clinical data to accurately classify gram-positive and gram-negative …

Lung cancer is a serious threat to human health, with millions dying because of its late
diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for …

Machine learning techniques have lately attracted a lot of attention for their potential to
execute expert-level clinical tasks, notably in the area of medical image analysis. Chest …

Multimodality refers to the utilization of different data types with different representational
modes. Medical and health data are becoming more and more multimodal. Emerging …

Abstract COVID-19 (Coronavirus Disease of 2019) is one of the most challenging healthcare
crises of the twenty-first century. The pandemic causes many negative impacts on all …

Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model
incorporating clinical variables with automated convolutional neural network (CNN) chest …