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Related Experiment Video

Updated: Jun 29, 2026

A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents
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A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents

Published on: July 16, 2020

Bridging engineering and neuro-oncology: a scalable FastAPI-deployed CNN framework for real-time explainable brain

Sajjad Nematzadeh1, Ferzat Anka2, Fatih Ciftci3,4,5

  • 1Department of Software Engineer, Engineering and Natural Sciences Faculty, Istanbul Topkapi University, Istanbul, Türkiye.

Frontiers in Neuroscience
|March 27, 2026
PubMed
Summary

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This study developed an AI framework for classifying brain tumors from MRI scans, achieving accurate and interpretable results for neuro-oncology. The system is deployable for real-time radiological workflows.

Area of Science:

  • Medical Imaging
  • Neuro-oncology
  • Artificial Intelligence

Background:

  • Automated brain tumor classification from MRI scans is challenging.
  • Need for reliable, transparent, and deployable AI tools in neuro-oncology.
  • Focus on timely tumor differentiation with practical usability.

Purpose of the Study:

  • Develop and deploy an AI-driven framework for brain tumor classification.
  • Ensure transparency and practical usability in radiological workflows.
  • Support timely tumor differentiation using deep learning.

Main Methods:

  • Developed a deep learning framework using convolutional neural networks (TensorFlow).
  • Trained and evaluated on 3,097 axial brain MRI images (glioma, meningioma, pituitary, normal).
Keywords:
MRI classificationbrain tumorconvolutional neural networkgrad-CAMmachine learning

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Last Updated: Jun 29, 2026

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  • Used 5-fold cross-validation, benchmarking against transfer learning models, FastAPI deployment, and Grad-CAM for explainability.

    • Main Results:

    • Achieved stable and competitive performance (accuracy, F1-score, AUC) with low inter-fold variance.
    • Transfer learning models showed strong performance; custom CNN suitable for real-time deployment.
    • FastAPI enabled low-latency inference and on-demand Grad-CAM visualizations.

    Conclusions:

    • Demonstrated feasibility of scalable, real-time deployment for deep learning-based brain tumor classification.
    • Framework integrates robust validation, benchmarking, and explainability.
    • Provides a practical pathway for AI in radiological workflows, emphasizing interpretability and deployment.