ArticleScientific reports2025
An efficient deep learning network for brain stroke detection using salp shuffled shepherded optimization.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China.JMIR medical informatics · 2026Article
- Integration of imaging and clinical biomarkers for cerebral infarction diagnosis via NeuroFusionNet.Frontiers in neuroscience · 2026Article
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Authors and funding
5 authors.
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Abstract
Brain strokes (BS) are potentially life-threatening cerebrovascular conditions and the second highest contributor to mortality. They include hemorrhagic and ischemic strokes, which vary greatly in size, shape, and location, posing significant challenges for automated identification. Magnetic Resonance Imaging (MRI) brain imaging using Diffusion Weighted Imaging (DWI) will show fluid balance changes very early. Due to their higher sensitivity, MRI scans are more accurate than Computed Tomography (CT) scans. Salp Shuffled Shepherded EfficientNet (S3ET-NET), a new deep learning model in this research work, could propose the detection of brain stroke using brain MRI. The MRI images are pre-processed by a Gaussian bilateral (GB) filter to reduce the noise distortion in the input images. The Ghost Net model derives suitable features from the pre-processed images. The extracted images will have some optimal features that were selected by applying the Salp Shuffled Shepherded Optimization (S3O) algorithm. The Efficient Net model is utilized for classifying brain stroke cases, such as normal, Ischemic stroke (IS), and hemorrhagic stroke (HS). According to the result, the proposed S3ET-NET attains a 99.41% reliability rate. In contrast to Link Net, Mobile Net, and Google Net, the proposed Ghost Net improves detection accuracy by 1.16, 1.94, and 3.14%, respectively. The suggested Efficient Net outperforms ResNet50, zNet-mRMR-NB, and DNN in the accuracy range, improving by 3.20, 5.22, and 4.21%, respectively.
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