What is it about?
It presents a feature learning technique that enhances the performance of machine learning techniques to detect brain tumor regions at pixel-level in a magnetic resonance imaging (MRI) brain scan. It utilizes the image filtering based feature extraction techniques (e.g., Laplacian, Gradient, and Sobel filters) to construct a feature space. We adapted the Brain Tumor Segmentation (BraTS 2015) datasets to develop and validate the proposed feature learning framework.
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This page is a summary of: Pixel-Level Feature Space Modeling and Brain Tumor Detection Using Machine Learning, December 2020, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/icmla51294.2020.00134.
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