Paper Contents
Abstract
Brain tumors are one of the most critical and life-threatening diseases affecting people worldwide.They occur due to the uncontrolled and abnormal growth of cells in the brain, which can lead tosevere neurological damage and, if left untreated, can be fatal. Early detection and accuratediagnosis of brain tumors are crucial for effective treatment and improving survival rates.Identifying abnormal tissues from normal brain tissues is a key challenge in brain tumor detection,requiring advanced medical imaging techniques and computational analysis.This research focuseson utilizing Magnetic Resonance Imaging (MRI) for brain tumor detection by applying deeplearning-based image processing techniques. MRI scans provide detailed images of brainstructures, making them highly effective for identifying tumors. However, raw MRI images oftencontain noise and variations that can affect detection accuracy. To address this, pre-processingtechniques such as noise removal, contrast enhancement, and normalization are applied to refinethe input images. To further improve the accuracy of tumor detection, data augmentationtechniques are used to increase the size of the training dataset. The study employs ConvolutionalNeural Networks (CNNs), a widely used deep learning architecture for image analysis, to extractmeaningful features from MRI scans. CNN models are trained to differentiate between normal andtumor-affected tissues by learning spatial patterns and structural variations in the images. Byintegrating these advanced image processing and deep learning techniques, this research aims todevelop a highly accurate and reliable brain tumor detection system.
Copyright
Copyright © 2025 Varshini Guttula. This is an open access article distributed under the Creative Commons Attribution License.
