An Automated System for Detecting and Classifying Brain Tumors Using Convolutional Neural Networks (VGG16)

Authors

  • Eman Bashir Alghwil Internet Technologies Department, Faculty of Information Technology, Alasmarya Islamic University, Zliten, Libya
  • Aisha Muftah Abughwila Internet Technologies Department, Faculty of Information Technology, Alasmarya Islamic University, Zliten, Libya
  • Sara Mansour Elgoud Computer Science Department, Faculty of Information Technology, Alasmarya Islamic University, Zliten, Libya

Keywords:

Brain Tumor Diagnosis, MRI Images, Deep Learning, Convolutional Neural Networks, VGG

Abstract

One of the most crucial strategies in managing brain tumors is early and accurate detection to enable timely intervention and stop their growth. In this research, it was research to use the deep convolutional neural network VGG-16, which is employed to extract deep features from brain MRI images from a dataset compiled from the (Kaggel) consisting of 7,023 MRI images These images were divided into four categories depending on the type main categories: glioma tumor, meningioma tumor, pituitary tumor, and healthy cases. Where the images are then passed through multiple convolutional layers, pooling layers, and fully connected layers to perform the final classification process. Where 80% of them were used for training, and 20% for testing. The final results of the accuracy obtained from the experiments of using the research mode is (95%).

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Published

2026-04-02

How to Cite

Eman Bashir Alghwil, Aisha Muftah Abughwila, & Sara Mansour Elgoud. (2026). An Automated System for Detecting and Classifying Brain Tumors Using Convolutional Neural Networks (VGG16). North African Journal of Scientific Publishing (NAJSP), 4(2), 08–15. Retrieved from https://najsp.com/index.php/home/article/view/811

Issue

Volume 4, Issue 2, 2026

Section

Applied and Natural Sciences

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.