<![CDATA[rain tumors are critical medical conditions that require early diagnosis to improve treatmentoutcomes. Magnetic Resonance Imaging (MRI) is widely utilized for brain tumor detection due to itsability to produce high-resolution images of soft tissues. Nevertheless, manual interpretation of MRIimages presents several challenges, including time inefficiency and variability among observers. Toaddress these issues, this study applies the Convolutional Neural Network (CNN) approach usingVGG-16 and Xception architectures to classify brain tumor MRI images and to evaluate theirperformance comparatively. The dataset comprises 2,877 MRI images categorized into four classes:glioma tumor, meningioma tumor, pituitary tumor, and no tumor. Preprocessing stages includeresizing images to 224×224 pixels and dividing the dataset into training, validation, and testing setswith a ratio of 80:10:10. Model performance is assessed using accuracy, precision, recall, and F1-scoremetrics. Experimental results indicate that the VGG-16 architecture achieves an accuracy of 92%, whilethe Xception architecture records an accuracy of 91%.]]>
Copyrights © 2026
