Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Article
Shoeibi, Afshin, Khodatars, Marjane, Jafari, Mahboobeh, Moridian, Parisa, Rezaei, Mitra, Alizadehsani, Roohallah, Khozeimeh, Fahime, Gorriz, Juan Manuel, Heras, Jónathan, Panahiazar, Maryam, Nahavandi, Saeid and Acharya, U. Rajendra. 2021. "Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review." Computers in Biology and Medicine. 136. https://doi.org/10.1016/j.compbiomed.2021.104697
| Article Title | Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review |
|---|---|
| ERA Journal ID | 5040 |
| Article Category | Article |
| Authors | Shoeibi, Afshin, Khodatars, Marjane, Jafari, Mahboobeh, Moridian, Parisa, Rezaei, Mitra, Alizadehsani, Roohallah, Khozeimeh, Fahime, Gorriz, Juan Manuel, Heras, Jónathan, Panahiazar, Maryam, Nahavandi, Saeid and Acharya, U. Rajendra |
| Journal Title | Computers in Biology and Medicine |
| Journal Citation | 136 |
| Article Number | 104697 |
| Number of Pages | 23 |
| Year | 2021 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0010-4825 |
| 1879-0534 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2021.104697 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482521004911 |
| Abstract | 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. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided. |
| Keywords | Deep learning; Diagnosis; MRI; Neuroimaging; Multiple sclerosis |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
| Byline Affiliations | K. N. Toosi University of Technology, Iran |
| Islamic Azad University, Iran | |
| Semnan University, Iran | |
| Tarbiat Modares University, Iran | |
| Deakin University | |
| University of Granada, Spain | |
| University of Cambridge, United Kingdom | |
| University of La Rioja, Spain | |
| University of California, United States | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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