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Related Experiment Video

Updated: Jan 10, 2026

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

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Enhanced Brain Tumor Classification with Convolutional Neural Networks.

Athanasios Kanavos1, Orestis Papadimitriou2, Gerasimos Vonitsanos3

  • 1Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece. icsdd20017@icsd.aegean.gr.

Advances in Experimental Medicine and Biology
|November 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method using convolutional neural networks (CNNs) for accurate brain tumor classification from medical images. The approach enhances diagnostic precision and treatment planning through automated tumor identification.

Keywords:
Automated tumor diagnosisBrain tumor classificationConvolutional neural networksDeep learningMedical image analysis

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate brain tumor classification is essential for effective patient treatment and diagnosis.
  • Current methods may lack the precision and automation needed for complex cases.

Purpose of the Study:

  • To develop and evaluate a deep learning methodology for automated brain tumor image classification.
  • To differentiate between various brain tumor types, including gliomas, meningiomas, and metastatic tumors.

Main Methods:

  • Utilized convolutional neural networks (CNNs) for autonomous feature extraction from brain tumor images.
  • Employed a CNN architecture with convolutional, pooling, and fully connected layers.
  • Enhanced model performance using data augmentation and hyperparameter tuning.

Main Results:

  • Achieved significant improvements in brain tumor classification accuracy.
  • Demonstrated the model's efficacy in differentiating between gliomas, meningiomas, and metastatic tumors.
  • Experimental evaluations confirmed the approach's effectiveness.

Conclusions:

  • The proposed CNN-based method offers accurate, automated brain tumor classification.
  • This approach has the potential to significantly enhance diagnostic processes in neuro-oncology.
  • Advances contribute to the broader application of machine learning in medical imaging for improved patient care.