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Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
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Multi-Chaotic HEOA for Hardware-Aware Neural Architecture Search: Brain Tumor Classification on FPGA.

Ismail Mchichou1, Hamza Tahiri2, Mohamed Amine Tahiri2

  • 1Laboratory of Electronic Signals and Systems of Information, Dhar El Mahrez Faculty of Science, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study introduces an optimized CNN architecture for brain tumor classification on embedded FPGAs using Multi-Chaotic Enhanced HEOA (MC-HEOA). The approach achieves high accuracy, demonstrating feasibility for real-time medical diagnosis systems.

Keywords:
FPGA implementationHEOAZynq-7000brain tumor classificationdeep learningembedded systemshigh-level synthesismedical image analysismulti-chaotic optimizationneural architecture search

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Area of Science:

  • Medical Imaging and Diagnostics
  • Computer Engineering and Hardware Acceleration
  • Artificial Intelligence and Machine Learning

Background:

  • Automated brain tumor classification from MRI scans is crucial for efficient diagnosis.
  • Deploying complex Convolutional Neural Network (CNN) architectures on resource-constrained embedded Field-Programmable Gate Array (FPGA) platforms presents significant challenges.
  • Optimizing CNNs for embedded systems requires novel approaches to architecture discovery and hardware compatibility.

Purpose of the Study:

  • To develop and validate an integrated approach for automatic CNN architecture discovery tailored for embedded FPGA deployment.
  • To enhance the efficiency and accuracy of brain tumor classification using optimized CNNs on FPGAs.
  • To demonstrate the feasibility of real-time medical diagnosis through hardware-accelerated AI models.

Main Methods:

  • Utilized Multi-Chaotic Enhanced HEOA (MC-HEOA) for automatic CNN architecture search, evaluating chaotic maps on a CEC2023 benchmark.
  • Explored a vast search space (1.31 × 10^16 configurations) for architectural choices (layers, convolutions, channels, pooling) with a parameter constraint (< 1 million) for FPGA compatibility.
  • Performed High-Level Synthesis (HLS) on a Xilinx Zynq-7000 FPGA to validate embedded deployment feasibility and performance.

Main Results:

  • The optimal discovered CNN architecture achieved 91.33% test accuracy and 92.44% validation accuracy on the 4-class Brain Tumor MRI dataset.
  • The architecture contains 724,200 parameters, meeting the FPGA compatibility constraint.
  • FPGA synthesis showed DSP utilization of 16%, LUT of 57%, FF of 28%, with an inference latency of 374 ms at 100 MHz, confirming deployment feasibility.

Conclusions:

  • MC-HEOA effectively discovers compact, high-performing CNN architectures suitable for FPGA deployment.
  • The study validates the practical application of embedded AI for real-time medical diagnosis, specifically in brain tumor classification.
  • This integrated approach opens new avenues for efficient, on-device medical imaging analysis.