Automatic and accurate segmentation of pulmonary nodules in CT images can help
physicians perform more accurate quantitative analysis, diagnose diseases, and improve …

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to
face an existential health crisis. Automated detection of lung infections from computed …

Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer
vision and image recognition creating widespread opportunities of using artificial …

The COVID‐19 pandemic is a global, national, and local public health concern which has
caused a significant outbreak in all countries and regions for both males and females …

Abstract Coronavirus Disease 2019 (COVID-19) has become one of the most urgent public
health events worldwide due to its high infectivity and mortality. Computed tomography (CT) …

Lung cancer is the leading cause of cancer deaths nowadays and its early detection and
treatment plays an important role in survival of patients. The main challenge is to acquire an …

The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground
Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for …

Semi-supervised learning (SSL) methods show their powerful performance to deal with the
issue of data shortage in the field of medical image segmentation. However, existing SSL …

Background Lung region segmentation is an important stage of automated image-based
approaches for the diagnosis of respiratory diseases. Manual methods executed by experts …

The global outbreak of COVID-19 has become an important research topic in healthcare
since 2019. RT-PCR is the main method for detecting COVID-19, but the long detection time …