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  1. Home
  3. Brain Tumor Classification In Mri Images Using Combined Transfer Learning And Convolutional Neural Networks.
  1. Home
  3. Brain Tumor Classification In Mri Images Using Combined Transfer Learning And Convolutional Neural Networks.

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Related Experiment Video

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Brain Tumor Classification in MRI Images Using Combined Transfer Learning and Convolutional Neural Networks.

Maisam Abbas1, Muhammad Hassan1, Ran-Zan Wang1

  • 1Department of Computer Science and Engineering, Yuan Ze University, Yuandong Rd., Zhongli District, Taoyuan 32003, Taiwan.

Journal of Imaging
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A novel Custom Convolutional Neural Network (CNN) excels at brain tumor classification from MRI scans, achieving 99.54% accuracy. This efficient deep learning model outperforms pre-trained networks and ensembles for early tumor detection.

Keywords:
Magnetic Resonance Imaging (MRI)brain tumor classificationconvolutional neural networkpre-trained modelstransfer learning

Related Experiment Videos

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Early and accurate detection of brain tumors is crucial for effective patient treatment and management.
  • Magnetic Resonance Imaging (MRI) is a primary modality for visualizing brain structures and identifying abnormalities.
  • Deep learning offers potential for automating and improving the accuracy of diagnostic processes in medical imaging.

Purpose of the Study:

  • To develop and evaluate a novel deep learning framework for accurate brain tumor classification using MRI data.
  • To compare the performance of the proposed Custom Convolutional Neural Network (CNN) against established pre-trained models and ensemble methods.
  • To assess the efficiency and effectiveness of a task-specific deep learning architecture for automated brain tumor detection.

Main Methods:

  • A novel Custom CNN architecture was designed and implemented for brain tumor classification.
  • The Custom CNN was evaluated independently against six pre-trained models: InceptionV3, EfficientNetV2L, ResNet152V2, Xception, VGG16, and MobileNetV2.
  • Three separate ensemble models were constructed to investigate the impact of model combination on classification performance.
  • Experiments were conducted using the Kaggle-Multiclass brain MRI dataset.

Main Results:

  • The proposed Custom CNN achieved the highest classification accuracy at 99.54%.
  • The Custom CNN demonstrated superior performance compared to individual pre-trained models and ensemble approaches due to its domain-specific architecture and feature learning.
  • Among pre-trained models, EfficientNetV2L (99.47%) and InceptionV3 (99.39%) showed competitive results.
  • The best ensemble model reached 99.47% accuracy, but the Custom CNN achieved better performance with greater computational efficiency (0.57M parameters).

Conclusions:

  • The developed Custom CNN offers a highly effective and computationally efficient solution for automated brain tumor classification from MRI scans.
  • The proposed model surpasses the performance of widely used pre-trained deep learning models and ensemble methods without added complexity.
  • These findings suggest significant potential for the Custom CNN in clinical settings for early and accurate brain tumor detection, pending external validation.