Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 13, 2026

Modeling Astrocytoma Pathogenesis In Vitro and In Vivo Using Cortical Astrocytes or Neural Stem Cells from Conditional, Genetically Engineered Mice
10:13

Modeling Astrocytoma Pathogenesis In Vitro and In Vivo Using Cortical Astrocytes or Neural Stem Cells from Conditional, Genetically Engineered Mice

Published on: August 12, 2014

13.9K

Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable

Christos Ch Andrianos1, Spiros A Kostopoulos1, Ioannis K Kalatzis1

  • 1Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, 12241 Athens, Greece.

Journal of Imaging
|October 28, 2025
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Segmentation-Free Preoperative 3D MRI Classification of Low-Grade Versus High-Grade Glioma Using Task-Oriented Neural Architecture Search.

Journal of imaging·2026
Same author

Implementation of Instrumental Analytical Methods, Image Analysis and Chemometrics for the Comparative Evaluation of Citrus Fruit Peels.

Foods (Basel, Switzerland)·2025
Same author

Prediction of remaining surgery duration based on machine learning methods and laparoscopic annotation data.

Biomedizinische Technik. Biomedical engineering·2025
Same author

Over 30 Years of DiI Use for Human Neuroanatomical Tract Tracing: A Scoping Review.

Biomolecules·2024
Same author

Single-cell and spatial transcriptomics analysis of non-small cell lung cancer.

Nature communications·2024
Same author

Acquisition of epithelial plasticity in human chronic liver disease.

Nature·2024
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
See all related articles
This summary is machine-generated.

This study developed an optimized Convolutional Neural Network (CNN) to accurately classify astrocytoma brain tumor grades from MRI scans. The AI model achieved high accuracy, showing potential for clinical use in cancer diagnosis.

Area of Science:

  • Neuro-oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Astrocytoma, a common brain glioma, is graded by the WHO, influencing prognosis and treatment.
  • Accurate grading is crucial for patient management, particularly for high-malignancy tumors.
  • Current grading methods can be subjective and time-consuming.

Purpose of the Study:

  • To develop and optimize a Convolutional Neural Network (CNN) for classifying astrocytoma MRI slices into malignant grades (G2-4).
  • To evaluate the CNN's performance against state-of-the-art models and assess its robustness for clinical deployment.

Main Methods:

  • A fully optimized CNN was trained on 1284 axial 2D MRI slices (T1-weighted contrast-enhanced and FLAIR) from 69 patients.
  • Extensive hyperparameter tuning was performed using the Hyperband method.
Keywords:
astrocytomaconvolutional neural networkexplainable artificial intelligencehyperparameters optimization

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

43.4K

Related Experiment Videos

Last Updated: Jan 13, 2026

Modeling Astrocytoma Pathogenesis In Vitro and In Vivo Using Cortical Astrocytes or Neural Stem Cells from Conditional, Genetically Engineered Mice
10:13

Modeling Astrocytoma Pathogenesis In Vitro and In Vivo Using Cortical Astrocytes or Neural Stem Cells from Conditional, Genetically Engineered Mice

Published on: August 12, 2014

13.9K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

43.4K
  • Repeated Hold-Out Validation across four data splits was employed, with SHAP and LIME for model interpretability.

    • Main Results:

    • The proposed CNN achieved a mean classification accuracy of 98.05% with an AUC of 0.997.
    • The model outperformed state-of-the-art pre-trained models using transfer learning.
    • Validation on unmodified slices yielded 93.34% accuracy, demonstrating robustness.

    Conclusions:

    • The optimized CNN demonstrates high accuracy and robustness in classifying astrocytoma grades from MRI data.
    • The AI model shows significant potential for clinical application in neuro-oncology.
    • Explainable AI techniques confirmed the model's decision-making process.