To enhance the accuracy of brain tumor diagnosis and treatment, reliance on MRI images is
crucial. However, human error in manual diagnosis remains a concern, underscoring the …

Lung cancer is the leading cause of cancer deaths worldwide. It is a type of cancer that
commonly remains undetected due to unpresented symptoms until it has progressed to later …

Magnetic resonance imaging (MRI) technology is crucial in the medical field, but the thermal
noise in the reconstructed MR images may interfere with the clinical diagnosis. Removing …

This study evaluates the impact of step size selection on Jacobian-based inverse kinematics
(IK) for robotic manipulators. Although traditional constant step size approaches offer …

Clinical diagnosis of breast malignancy (BM) is a challenging problem in the recent era. In
particular, Deep learning (DL) models have continued to offer important solutions for early …

This paper investigates spatial computing, which is a pathological transformational modern
technology that integrates the physical and digital realms and has the potential to …

This paper demonstrates that simplified Convolutional Neural Network (CNN) models can
outperform traditional complex architectures, such as VGG-16, in the analysis of radiological …

The concept of geosites and geodiversity sites that document selected elements of
geodiversity has proved to be extremely useful in the preservation and communication of the …

Recent advancements in medical image segmentation have been driven by deep learning's
capability to extract rich features from extensive datasets. However, these improvements rely …

Background: Accurate and reliable classification models play a major role in clinical
decision-making processes for prostate cancer (PCa) diagnosis. However, existing methods …