Classification of Brain Tumor based on Machine Learning Algorithms: A Review

Abstract

Brain tumor classification using machine learning algorithms is pivotal for medical diagnostics, particularly in magnetic resonance imaging (MRI) analysis. This review provides a comprehensive overview of recent advancements in brain tumor classification methodologies, emphasizing preprocessing, feature extraction, and classification phases. Preprocessing steps involve noise reduction and intensity normalization, while feature extraction encompasses texture analysis and deep learning-based methods. Machine learning algorithms such as support vector machines (SVM) and convolutional neural networks (CNNs) are utilized for accurate classification based on extracted features. Recent literature highlights the significance of diverse datasets, hyperparameter tuning, and segmentation techniques in improving diagnostic accuracy. Noteworthy methodologies include deep learning models for glioma grading and novel optimization techniques for tumor segmentation. The review underscores interdisciplinary collaboration between medical professionals and computational scientists to refine existing methodologies and overcome challenges. A systematic search of academic databases, including PubMed, IEEE Xplore, ACM Digital Library, ScienceDirect, Springer and Google Scholar, was conducted. Continued evolution in brain tumor classification promises enhanced diagnostic accuracy and personalized treatment strategies, ultimately improving patient outcomes in neuro-oncology.

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