The use of deep learning for identifying defects in medical images has rapidly emerged as a
significant area of interest across various medical diagnostic applications. The automated …

Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In
this review, we discuss how AI can assist in brain tumour imaging, focusing on machine …

Simple Summary This paper focuses on interpreting machine learning (ML) models'
decisions in medical diagnoses, specifically for four types of posterior fossa tumors in …

In the realm of pediatric brain tumors, the integration of artificial intelligence (AI) has
catalyzed a paradigm shift, enriching various facets of clinical care, from detection and …

Background/Objectives: Artificial intelligence is transforming neuroimaging by enhancing
diagnostic precision and treatment planning. However, its applications in pediatric cancer …

Early detection is necessary to achieve better patient outcomes because tumours are the
second most common cause of cancer. Brain tumours can result in the formation of aberrant …

University of Birmingham MIROR: The clinical decision support system with functional imaging
and machine learning Page 1 University of Birmingham MIROR: The clinical decision support …

Motivation: Accurate classification of multi-type brain tumours through in vivo proton
magnetic resonance spectroscopy remains a significant challenge. Conventional machine …