Brain Tumor Detection Using PCA And Neural Networks From MRI Images
DOI:
https://doi.org/10.69996/fmep.2026010Keywords:
Brain Tumor Detection, Magnetic Resonance Imaging (MRI), Principal Component Analysis (PCA), Neural Networks, Image ProcessingAbstract
Early diagnosis improves treatment outcomes, making brain tumor detection a crucial task in medical image analysis. In this study, we offer a method for detecting brain tumors using MRI scans. In order to improve MRI pictures, the suggested system does preprocessing operations such converting to grayscale, scaling, and histogram equalization. In order to extract useful features, one uses Principal Component Analysis (PCA) to lower the dimensionality and capture the most important ones. A neural network classifier is trained using these variables to categorize MRI scans as normal, benign, or malignant based on the presence or absence of tumors.
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