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

arXiv: https://arxiv.org/abs/2105.04881 · HTML: https://mlsanity.com/paper/2105.04881/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 236 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 30 of 42)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Zero-Shot Learning Through Cross-Modal Transfer](https://mlsanity.com/paper/1301.3666/) (2013)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Deep representation learning for human motion prediction and classification](https://mlsanity.com/paper/1702.07486/) (2017)
- [An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning](https://mlsanity.com/paper/1611.08331/) (2016)
- [A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis](https://mlsanity.com/paper/2005.04573/) (2020)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection](https://mlsanity.com/paper/1803.11078/) (2018)
- [Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation](https://mlsanity.com/paper/1904.08771/) (2019)
- [One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks](https://mlsanity.com/paper/1805.12415/) (2018)
- [3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study](https://mlsanity.com/paper/1612.03925/) (2016)
- [Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach](https://mlsanity.com/paper/1702.04869/) (2017)
- [Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/1811.02942/) (2018)
- [Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks](https://mlsanity.com/paper/1805.06349/) (2018)
- [Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET](https://mlsanity.com/paper/1901.05733/) (2019)
- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI](https://mlsanity.com/paper/1910.10035/) (2019)
- [Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)](https://mlsanity.com/paper/2008.10114/) (2020)
- [Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation](https://mlsanity.com/paper/1901.09263/) (2019)
- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2002.12470/) (2020)
- [Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks](https://mlsanity.com/paper/1803.09172/) (2018)
- [Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training](https://mlsanity.com/paper/1804.08039/) (2018)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis](https://mlsanity.com/paper/1809.03185/) (2018)

## Built on by (most cited first, 10 of 10)

- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust](https://mlsanity.com/paper/2209.10877/) (2022)
- [Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression](https://mlsanity.com/paper/2205.15858/) (2022)
- [ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation -- Methods and Results](https://mlsanity.com/paper/2410.07924/) (2024)
- [OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation](https://mlsanity.com/paper/2508.17524/) (2025)
- [Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images](https://mlsanity.com/paper/2110.14013/) (2021)
- [Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression](https://mlsanity.com/paper/2109.05461/) (2021)
- [What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors](https://mlsanity.com/paper/2109.05457/) (2021)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border Collaboration](https://mlsanity.com/paper/2410.02443/) (2024)

## 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.