Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease
containment decisions. In this study, we demonstrate how transfer learning from deep …

Intracranial hemorrhage is a critical conditional with the high mortality rate that is typically
diagnosed based on head computer tomography (CT) images. Deep learning algorithms, in …

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease
containment decisions. In this study, we demonstrate how pre-trained deep learning models …

Deep learning (DL) is a popular method that is used to perform many important tasks in
radiology and medical imaging. Some forms of DL are able to accurately segment organs …

Segmentation of lung fields is an important pre-requisite step in chest radiographic computer-
aided diagnosis systems as it precisely defines the region-of-interest on which different …

Remote image semantic segmentation technology is one of the core research elements in
the field of computer vision and has a wide range of applications in production life. Most …

Image segmentation is a method to extract regions of interest from an image. It remains a
fundamental problem in computer vision. The increasing diversity and the complexity of …

Deep neural networks have entirely dominated the machine vision space in the past few
years due to their astonishing human comparable performance. This paper applies power of …

Cervical tumor segmentation on 3D 18 FDG PET images is a challenging task because of
the proximity between cervix and bladder, both of which can uptake 18 FDG tracers. This …