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Related Experiment Video

Updated: Jun 27, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

A deep ensemble learning framework for brain tumor classification using data balancing and fine-tuning.

Md Alamin Talukder¹, Md Manowarul Islam², Md Ashraf Uddin³

¹Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh. alamintalukder.cse.jnu@gmail.com.

Scientific Reports

|October 9, 2025

Summary

This summary is machine-generated.

This study introduces a deep ensemble model using transfer learning (TL) for accurate brain tumor classification from MRI scans. The optimized model achieved 99.84% accuracy, aiding in precise and timely diagnoses.

Keywords:

Brain magnetic resonance imaging Classification Deep learning Ensemble Optimized Transfer learning

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Published on: August 16, 2020

Related Experiment Videos

Last Updated: Jun 27, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Accurate brain tumor diagnosis is crucial for patient outcomes, but manual MRI analysis is time-consuming.
Deep learning (DL) offers potential for efficient and accurate diagnostic assistance.
Misclassification of brain tumors can lead to reduced life expectancy, highlighting the need for precise methods.

Purpose of the Study:

To develop and evaluate an innovative deep ensemble approach for brain tumor classification using transfer learning (TL).
To enhance diagnostic accuracy and efficiency in analyzing brain tumor MRI datasets.
To compare the performance of the proposed model against existing state-of-the-art methods.

Main Methods:

A deep ensemble model was developed using transfer learning (TL) architectures.
Preprocessing, synthetic data generation (SDG), and fine-tuning of TL models were performed.
Model weights were optimized using Genetic Algorithm-based Weight Optimization (GAWO) and Grid Search-based Weight Optimization (GSWO).

Main Results:

The proposed deep ensemble model achieved high classification accuracies, with GSWO reaching 99.84%.
Individual TL models like Xception and ResNet variants also showed strong performance (99.57% - 99.33%).
The model demonstrated superiority over State of Arts (SOA) works in comparative analysis.

Conclusions:

The optimized deep ensemble model is a robust and reliable tool for brain tumor classification.
This approach can significantly assist neurologists and clinicians in making precise and timely diagnostic decisions.
The study underscores the potential of advanced DL techniques in medical diagnostics.