Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
Ashimgaliyev M. Matkarimov B. Barlybayev A. Li R.Y.M. Zhumadillayeva A.
August 2024Multidisciplinary Digital Publishing Institute (MDPI)
Applied Sciences (Switzerland)
2024#14Issue 16
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.
brain neoplasm , categorisation , ensemble algorithm , image segmentation , magnetic resonance imaging , neural networks
Text of the article Перейти на текст статьи
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
Department of Computer Engineering, Astana IT University, Astana, 010000, Kazakhstan
Faculty of Information Technologies
Higher School of Information Technology and Engineering
Department of Economics and Finance
Department of Computer Engineering
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026