Video-based human activity recognition (HAR) is an important task in many fields, such as
healthcare monitoring, video surveillance, and sports analysis. This review paper aims to …

Computed tomography (CT) is used in a wide range of medical imaging diagnoses.
However, the reconstruction of CT images from raw projection data is inherently complex …

The segmentation of the liver is a difficult process due to the changes in shape, border, and
density that occur in each section in computed tomography (CT) images. In this study, the …

Medical image analysis requires liver segmentation for liver disease detection and
treatment. Deep learning approaches, particularly liver segmentation, have demonstrated …

The healthcare industry faces significant challenges in patient identification and data
interoperability due to the use of different electronic medical records (EMRs) and variations …

In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to
enhance the classification of nodules and non-nodules in chest radiographs (CXRs). Our …

Human-human interaction recognition is crucial in computer vision fields like surveillance,
human-computer interaction, and social robotics. It enhances systems' ability to interpret and …

Segmenting the liver and liver tumors in computed tomography (CT) images is an important
step toward quantifiable biomarkers for a computer-aided decision-making system and …

Recent advancements in 3D deep learning have led to significant progress in improving
accuracy and reducing processing time, with applications spanning various domains such …

Pediatric brain tumors are the second most common type of cancer, accounting for one in
four childhood cancer types. Brain tumor resection surgery remains the most common …