Volume 13, Issue 6

A Comprehensive Review of Brain Tumor Detection Using Advanced Machine Learning and Deep Learning Methods

Author

Palvadi Srinivas Kumar & Dr. Rajesh Kumar

Abstract

Accurate cancer identification is essential for therapy selection, treatment monitoring, and prognostic evaluation. Convolutional Neural Networks (CNNs are widely employed in medical image analysis due to their ability to automatically extract hierarchical features through convolutional and pooling operations. Extreme Learning Machines (ELMs), characterized by fast training and single hidden-layer architectures, have demonstrated effectiveness in classification and regression tasks. Gliomas, the most common and aggressive primary brain tumors, significantly affect patient survival, emphasizing the need for precise treatment planning. Magnetic Resonance Imaging (MRI) is the primary imaging modality for brain tumor diagnosis; however, the high dimensionality and large volume of MRI data limit the efficiency of conventional manual analysis. Therefore, reliable and automated tumor segmentation and classification techniques are critical for improving diagnostic accuracy and clinical decision-making.

DOI: https://doi.org/10.62226/ijarst20262735

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DOI

https://doi.org/10.62226/ijarst20262735

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