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  1. Home
  3. An Efficient Deep Learning Network For Brain Stroke Detection Using Salp Shuffled Shepherded Optimization.
  1. Home
  3. An Efficient Deep Learning Network For Brain Stroke Detection Using Salp Shuffled Shepherded Optimization.

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An efficient deep learning network for brain stroke detection using salp shuffled shepherded optimization.

Xingsi Xue1,2, S E Viswapriya3, D Rajeswari4

  • 1College of Artificial Intelligence, Yango University, Fuzhou, 350015, Fujian, China.

Scientific Reports
|September 29, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A novel deep learning model, S3ET-NET, accurately detects brain strokes using MRI scans. This method achieves 99.41% reliability, improving early diagnosis of ischemic and hemorrhagic stroke.

Keywords:
Brain strokeEfficient netGaussian bilateralGhost netSalp shuffled shepherded

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Brain strokes (BS) are critical cerebrovascular conditions and a leading cause of mortality.
  • Hemorrhagic and ischemic strokes present diagnostic challenges due to variations in size, shape, and location.
  • Diffusion Weighted Imaging (DWI) in Magnetic Resonance Imaging (MRI) offers early detection of fluid balance changes, surpassing Computed Tomography (CT) accuracy.

Purpose of the Study:

  • To introduce a novel deep learning model, Salp Shuffled Shepherded EfficientNet (S3ET-NET), for automated brain stroke detection using MRI.
  • To enhance the accuracy and reliability of brain stroke classification, differentiating between normal, ischemic stroke (IS), and hemorrhagic stroke (HS).

Main Methods:

  • MRI images were pre-processed using a Gaussian bilateral (GB) filter to minimize noise.
  • Feature extraction was performed using the Ghost Net model.
  • Optimal features were selected via the Salp Shuffled Shepherded Optimization (S3O) algorithm.
  • Classification of brain stroke types was conducted using the Efficient Net model.

    • Main Results:

    • The S3ET-NET model achieved a high reliability rate of 99.41%.
    • The Ghost Net component demonstrated improved detection accuracy compared to Link Net, Mobile Net, and Google Net.
    • The Efficient Net classifier outperformed ResNet50, zNet-mRMR-NB, and DNN in accuracy.

    Conclusions:

    • The proposed S3ET-NET model offers a highly reliable and accurate method for brain stroke detection from MRI scans.
    • The integration of Ghost Net and Efficient Net with the S3O algorithm significantly enhances diagnostic performance.
    • This deep learning approach holds promise for improving early and accurate diagnosis of various stroke types.