Background: The major challenge in medical imaging is to achieve high accuracy output
during semantic image segmentation tasks in biomedical imaging while having fewer …

Background: Accurate semantic segmentation of kidney tumors in computed tomography
(CT) images is difficult because tumors feature varied forms and occasionally, look alike …

Introduction Kidney tumors are common cancer in advanced age, and providing early
detection is crucial. Medical imaging and deep learning methods are increasingly attractive …

Deep learning techniques have gained prominence in the realm of medical imaging,
especially for kidney tumor segmentation and classification. However, there have been …

Kidney tumors pose a significant health concern, particularly in senior age groups, and are a
leading cause of morbidity and mortality. Current clinical practices rely on noninvasive …

Accurate segmentation of the kidney anatomy is crucial in the diagnosis and treatment of
various kidney diseases. However, 3D U-Net-based Neural Networks entail significant …

Today, kidney medical imaging has become the backbone for health professionals in
diagnosing kidney disease and determining its severity. Physicians commonly use …

The number of kidney cancer patients is increasing each year. Computed Tomography (CT)
scans of the kidneys are useful to assess tumors and study tumor morphology. Semantic …

Kidney Cancer is one of the most prevalent diseases that is more common in men than
women. Detecting kidney tumors at an early stage has been found to increase survival rates …

Semantic segmentation of biomedical images found its niche in screening and diagnostic
applications. Recent methods based on deep learning convolutional neural networks have …