In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Deep learning-based methods, in particular, convolutional neural networks and fully
convolutional networks are now widely used in the medical image analysis domain. The …

Brain stroke is one of the most leading causes of worldwide death and requires proper
medical treatment. Therefore, in this paper, our aim is to classify brain computed tomography …

Accurate diagnosis of brain stroke, classification and segmentation of the stroke are
extremely important for physicians to focus on specific points of the brain and apply the right …

Abstract Machine learning and computer vision techniques have influenced many fields
including the biomedical one. The aim of this paper is to investigate the important concept of …

Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of
computed tomography (CT) for early detection of liver cancer could save millions of lives per …

Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the
complementary multi-phase information for liver tumor segmentation (LiTS), which are …

Purpose Liver tumor segmentation is a crucial prerequisite for computer‐aided diagnosis of
liver tumors. In the clinical diagnosis of liver tumors, radiologists usually examine multiphase …

Multi-phase computed tomography (CT) images provide crucial complementary information
for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods …

Deep learning has become a thriving force in the computer aided diagnosis of liver cancer,
as it solves extremely complicated challenges with high accuracy over time and facilitates …