mlsanityData to 7 Oct 2026

Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

arXiv 2105.04881 ↗First posted 11 May 2021eess.IV cs.CVcited by 10

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Abstract (arXiv)

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Hence, computer aided diagnosis systems (CADS) based on artificial intelligence (AI) methods have been proposed in recent years for accurate diagnosis of MS using MRI neuroimaging modalities. In the AI field, automated MS diagnosis is being conducted using (i) conventional machine learning and (ii) deep learning (DL) techniques. The conventional machine learning approach is based on feature extraction and selection by trial and error. In DL, these steps are performed by the DL model itself. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities are discussed. Also, each work is thoroughly reviewed and discussed. Finally, the most important challenges and future directions in the automated MS diagnosis using DL techniques coupled with MRI modalities are presented in detail.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 42 of its 236 references that are in the map. First posted on arXiv 11 May 2021.

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42 of 236 references.

References · 42

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30 more
  1. 2018Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detectioncited by 22
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Cited by year

Citing papers per year: 2021: 4; 2022: 3; 2023: 0; 2024: 2; 2025: 1.

10 papers.

  1. 2021Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropiescited by 9
  2. 2022Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trustcited by 6
  3. 2022Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regressioncited by 4
  4. 2024ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation -- Methods and Resultscited by 4
  5. 2021Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Imagescited by 2
  6. 2025OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretationcited by 2
  7. 2021Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regressioncited by 1
  8. 2021What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectorscited by 0
  9. 2022Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Reviewcited by 0
  10. 2024Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border Collaborationcited by 0