Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …

Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …

We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …

We propose a novel attention gate (AG) model for medical imaging that automatically learns
to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly …

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular
carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor …

The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian
cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on …

Stereo matching algorithms usually consist of four steps, including matching cost calculation,
matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN …

Convolutional neural networks (CNNs) have revolutionized medical image analysis over the
past few years. The U-Net architecture is one of the most well-known CNN architectures for …

Automated water body detection from satellite imagery is a fundamental stage for urban
hydrological studies. In recent years, various deep convolutional neural network (DCNN) …

We describe a deep learning approach for automated brain hemorrhage detection from
computed tomography (CT) scans. Our model emulates the procedure followed by …