Accurate brain tumor segmentation is critical for diagnosis and treatment planning, whereby
multi-modal magnetic resonance imaging (MRI) is typically used for analysis. However …

Simple Summary Within the rapidly evolving landscape of Machine Learning in the medical
field, this paper focuses on the forefront advancements in neuro-oncological radiology. More …

Purpose A brain tumor is defined as any group of atypical cells occupying space in the brain.
There are more than 120 types of them. MRI scans are used for brain tumor diagnosis since …

This research presents a new method for estimating the wear scar in ball-on-disk
experiments using deep learning. The wear scar on the ball in this context has traditionally …

Gliomas observed in medical images require expert neuro-radiologist evaluation for
treatment planning and monitoring, motivating development of intelligent systems capable of …

Purpose Brain tumors are among the most lethal forms of cancer, so early diagnosis is
crucial. As a result of machine learning algorithms, radiologists can now make accurate …

Tumor segmentation is a valuable tool for gaining insights into tumors and improving
treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning …

The development of brain tumor cells causes the intracranial pressure inside the skull to
rise, which ultimately threatens the minuscular life cycle. So, medication is the only way to …

The cancer disease prediction and detection processes are crucial tasks in this emerging
world and it is tough to manage the diseases. Generally, the disease prediction processes …

Brain tumour is an abnormal growth of cells in the brain, and is a harmful and life-
threatening disease worldwide. The rapid development of tumour cells increases the illness …