Purpose. The need for computerized medical assistance for accurate detection of brain
hemorrhage from Computer Tomography (CT) images is more mandatory than conventional …

Deep learning algorithms have recently been applied for image detection and classication,
lately with good results in the medicine such as medical image analysis. This paper aims to …

Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is
of utmost importance to avoid untoward incidents that may even lead to death. Hence, this …

Deep learning algorithms have recently been applied to solving challenging problems in
medicine such as medical image classification and analysis. In some areas, those …

Diagnosing Intracranial Hemorrhage (ICH) at an early stage is difficult since it affects the
blood vessels in the brain, often resulting in death. We propose an ensemble of …

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very
dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT …

Abstract Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has
now been identified as a major cause of death after trauma if it is not adequately diagnosed …

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 …

We propose an approach to diagnosing brain hemorrhage by using deep learning. In
particular, three types of convolutional neural networks that are LeNet, GoogLeNet, and …

Artificial intelligence is a rapidly evolving field, with modern technological advances and the
growth of electronic health data opening new possibilities in diagnostic radiology. In recent …