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Related Experiment Video

Updated: Sep 13, 2025

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BrainCNN: Automated brain tumor grading from magnetic resonance images using a convolutional neural network-based

Jing Yang1, Muhammad Abubakar Siddique2, Hafeez Ullah2

  • 1Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya 50603 Kuala Lumpur, Malaysia.

SLAS Technology
|July 25, 2025
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Summary

This study developed an automated deep learning system for brain tumor grading using MRI scans. The novel approach achieved over 99% accuracy in classifying low-grade and high-grade brain tumors.

Keywords:
Brain tumor gradingConvolutional neural networkMRI

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate brain tumor grading is critical for treatment and survival.
  • Magnetic Resonance Imaging (MRI) is a key diagnostic tool.
  • Automated grading systems can improve efficiency and accuracy.

Purpose of the Study:

  • To develop an automated brain tumor grading system using deep learning.
  • To classify brain tumors into low-grade (LGT) and high-grade (HGT) categories.
  • To enhance diagnostic accuracy and computational efficiency.

Main Methods:

  • Utilized a dataset of 293 MRI scans.
  • Developed a specialized Convolutional Neural Network (CNN) integrated with pre-trained models.
  • Experimented with raw MRI slices, segmented tumor areas, and extracted features.
  • Employed machine learning models including SVM, MobileNet, Inception V3, ResNet-50, and a custom CNN.

Main Results:

  • Achieved a peak accuracy of 99.45% for brain tumor grading.
  • Demonstrated high classification accuracies: 99.56% for LGT and 99.49% for HGT.
  • Outperformed traditional methods in accuracy.
  • Improved computational efficiency by reducing processing time.

Conclusions:

  • The proposed deep learning model offers a highly accurate and efficient solution for automated brain tumor grading.
  • This automated system has the potential to significantly improve patient treatment planning and outcomes.
  • The integration of CNNs with MRI analysis represents a promising advancement in neuro-oncology diagnostics.