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

This work demonstrates the feasibility of bridging deep learning-based brain tumor classification with scalable, real-time deployment. By combining robust cross-validation, state-of-the-art benchmarking, and explainability-aware inference, the proposed framework provides a practical pathway toward integrating artificial intelligence into radiological workflows, while highlighting the importance of interpretability and deployment constraints in neuro-oncological applications.

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Publication Details

Journal
Frontiers in Neuroscience
Published
2026-03-11
DOI
https://doi.org/10.3389/fnins.2026.1772429
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

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

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

Ali Can Özarslan, Emir Oncu, Kadriye Yasemin Usta Ayanoğlu, Sajjad Nematzadeh, Fatih Ciftci, Ferzat Anka
article en

Abstract

This work demonstrates the feasibility of bridging deep learning-based brain tumor classification with scalable, real-time deployment. By combining robust cross-validation, state-of-the-art benchmarking, and explainability-aware inference, the proposed framework provides a practical pathway toward integrating artificial intelligence into radiological workflows, while highlighting the importance of interpretability and deployment constraints in neuro-oncological applications.

Frontiers in NeuroscienceVol. 20
Technische Universität Darmstadt (DE), Istanbul University-Cerrahpaşa (TR), Software Research and Development Consulting (TR), Bioengineering Center (RU), Fatih Sultan Mehmet Waqf University (TR), Fatih Sultan Mehmet Eğitim Ve Araştırma Hastanesi (TR), Istanbul University (TR)
Openalex Percentile: Top 42%
Brain Tumor Detection and Classification
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