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Updated: Apr 8, 2026

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
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Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

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Deep Learning Approaches for Multiple Sclerosis Detection in MRI Images.

Mohamad Marwan El Sidani1, Rita Younes1, Charles Yaacoub2

  • 1Faculty of Engineering, Université La Sagesse, Beirut, Lebanon.

Biomed Research International
|April 7, 2026
PubMed
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AI-driven balance evaluation: a comparative study between blind and non-blind individuals using the mini-BESTest.

PeerJ. Computer science·2025
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Deep learning models can automate multiple sclerosis (MS) lesion detection in MRI scans. VGG16 achieved the highest performance, while ResNet-10 offers efficiency for clinical diagnostics.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Multiple sclerosis (MS) diagnosis relies on MRI, but manual interpretation is slow and variable.
  • Automating MS lesion detection in MRI is crucial for efficient and accurate diagnostics.

Purpose of the Study:

  • To evaluate deep learning (DL) models for automated detection of MS lesions in MRI.
  • To compare the performance of four CNN architectures: AlexNet, VGG16, ResNet-10, and DenseNet-121.

Main Methods:

  • Utilized four modified CNN architectures (AlexNet, VGG16, ResNet-10, DenseNet-121) on 2831 MRI slices.
  • Preprocessed data included normalization, resizing, and augmentation.
  • Trained models using Adam optimizer and binary cross-entropy loss, evaluated with accuracy, precision, recall, and F1-score.
Keywords:
CNNDLMRIMSResNetVGG16medical imaging

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Main Results:

  • VGG16 achieved the highest AUC (0.94), precision (0.81), recall (0.93), and F1-score (0.86).
  • DenseNet-121 (AUC 0.91) and ResNet-10/AlexNet-C (AUC 0.90) also showed strong performance.
  • ResNet-10 offered the best efficiency-performance balance, suitable for resource-constrained settings.

Conclusions:

  • DL models show significant potential for automating MS lesion detection in clinical settings.
  • VGG16 and DenseNet-121 offer high accuracy, while ResNet-10 provides a practical balance for deployment.
  • Automated detection can lead to more reliable, efficient, and scalable MS diagnostics.