Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · Advanced Neural Network Applications · Machine Learning and ELM
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010008, Kazakhstan
Higher School of Information Technology and Engineering, Astana International University, Astana 010008, Kazakhstan
Department of Economics and Finance, Hong Kong Shue Yan University, Hong Kong, China
Department of Computer Engineering, Astana IT University, Astana 010000, Kazakhstan
Funding
Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan
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