Abstract:
Magnetic Resonance Imaging (MRI) is vital in diagnosing brain tumours, offering crucial insights into tumour morphology and precise localisation. Despite its pivotal role, accurately classifying
brain tumours from MRI scans is inherently complex due to their heterogeneous characteristics. This
study presents a novel integration of advanced segmentation methods with deep learning ensemble
algorithms to enhance the classification accuracy of MRI-based brain tumour diagnosis. We conduct
a thorough review of both traditional segmentation approaches and contemporary advancements
in region-based and machine learning-driven segmentation techniques. This paper explores the
utility of deep learning ensemble algorithms, capitalising on the diversity of model architectures
to augment tumour classification accuracy and robustness. Through the synergistic amalgamation
of sophisticated segmentation techniques and ensemble learning strategies, this research addresses
the shortcomings of traditional methodologies, thereby facilitating more precise and efficient brain
tumour classification.
