Brain tumors are highly hazardous, and precise automated segmentation of brain tumor
subregions has great importance and research significance on the diagnosis and treatment …

In conventional robot-assisted minimally invasive procedures (RMIS), surgeons have narrow
visual and complex working spaces, along with specular reflection, blood, camera-lens …

Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an
essential step in diagnosis and treatment planning to maximize the likelihood of successful …

Transforming Healthcare with Artificial Intelligence in Pakistan: A Comprehensive Overview
Page 1 Healthcare with Artificial Intelligence Pak Armed Forces Med J 2023; 73(4): 955 …

In recent years, the realms of computer vision and deep learning have ushered in
transformative changes across various domains. Among these, deep learning stands out for …

As a non-invasive, low-cost medical imaging technology, magnetic resonance imaging
(MRI) has become an important tool for brain tumor diagnosis. Many scholars have carried …

Brain tumour segmentation employing MRI images is important for disease diagnosis,
monitoring, and treatment planning. Till now, many encoder‐decoder architectures have …

Convolutional network models have been widely used in image segmentation. However,
there are many types of boundary contour features in medical images which seriously affect …

This research is to propose a training strategy for 2D U‐Net is proposed that uses selective
data augmentation technique to overcome the class imbalance issue. This also helps in …

Background The segmentation and recognition of pancreatic tumors are crucial tasks in the
diagnosis and treatment of pancreatic diseases. However, due to the relatively small …