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Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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  1. Home
  3. Brain Tumor Classification In Mri Scans Using Edge Computing And A Shallow Attention-guided Cnn.
  1. Home
  3. Brain Tumor Classification In Mri Scans Using Edge Computing And A Shallow Attention-guided Cnn.

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Brain Tumor Classification in MRI Scans Using Edge Computing and a Shallow Attention-Guided CNN.

Niraj Anil Babar1, Junayd Lateef1, ShahNawaz Syed1

  • 1Sensor Signal and Information Processing (SenSIP) Center, Arizona State University, Arizona, AZ 85281, USA.

Biomedicines
|October 29, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an efficient attention-guided deep learning model for brain tumor classification using MRI scans, achieving high accuracy with reduced model size for practical medical applications.

Keywords:
biomedical image processingconvolutional neural networksedge computingimage classificationmagnetic resonance imagingmodel compression

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Brain tumors necessitate accurate Magnetic Resonance Imaging (MRI) for diagnosis and treatment.
  • Deep learning (AI) shows promise in MRI analysis but often results in large, slow models unsuitable for edge computing.
  • Efficient AI models are crucial for practical clinical deployment in brain tumor classification.

Purpose of the Study:

  • To develop a novel, lightweight attention-guided classification model for brain tumors.
  • To investigate methods for reducing model parameters without compromising diagnostic accuracy.
  • To enhance the practicality of AI-driven brain tumor analysis for medical edge computing.

Main Methods:

  • A shallow attention-guided convolutional neural network (ANSA_Ensemble) was developed.
  • Model compression techniques and Monte Carlo simulations were employed for evaluation.
  • The model was validated on three diverse, open-source brain tumor datasets.

    • Main Results:

    • The ANSA_Ensemble model achieved high accuracies: 98.04% (best) and 96.69% (average) on the Cheng dataset.
    • Comparable results were obtained on the Bhuvaji (95.16%) and Sherif (95.20%) datasets.
    • Depthwise separable convolutions offered the optimal balance between accuracy and speed.

    Conclusions:

    • The proposed attention-guided model demonstrates performance on par with state-of-the-art methods.
    • Increasing attention blocks consistently improved model accuracy.
    • The study provides an efficient AI solution for brain tumor classification, with publicly available code.