Abstract Artificial Intelligence (AI) and Deep Learning have emerged as revolutionary tools
in the domain of cancer screening and risk assessment. Leveraging vast amounts of data …

Artificial Intelligence (AI) is revolutionizing healthcare by transforming disease identification,
treatment, and management. Healthcare organizations are rapidly adopting AI technologies …

According to experts in neurology, brain tumours pose a serious risk to human health. The
clinical identification and treatment of brain tumours rely heavily on accurate segmentation …

Abstract The Augmented Transformer U-Net (AugTransU-Net) is proposed to address
limitations in existing transformer-related U-Net models for brain tumor segmentation. While …

Thyroid nodule (TYN) is a life-threatening disease that is commonly observed among adults
globally. The applications of deep learning in computer-aided diagnosis systems (CADs) for …

Although fully automated volumetric approaches for monitoring brain tumor response have
many advantages, most available deep learning models are optimized for highly curated …

Accurate segmentation of brain tumors from MRI (Magnetic Resonance Imaging) sequences
is essential across diverse clinical scenarios, facilitating precise delineation of anatomical …

In the rapidly evolving landscape of education, the integration of Big Data and AI presents
significant opportunities for improving educational outcomes, especially in the context of …

Diffuse gliomas are the most common adult primary brain tumours [1]. This tumour family has
undergone further revision in the latest 2021 WHO classification of CNS tumours, now …

This study investigates the impact of advanced computational methodologies on brain tumor
segmentation in medical imaging, addressing challenges like interobserver variability and …