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Review

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

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Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
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Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
3
Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Anne Beck
Biology 2022, 11(3), 469; https://doi.org/10.3390/biology11030469
Received: 13 February 2022 / Revised: 12 March 2022 / Accepted: 14 March 2022 / Published: 18 March 2022
This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Various challenges occur in detecting different stages of the disorders. A limited amount of labeled and unlabeled datasets and other limitations is represented in this article to assist them in finding out the directions. The authors’ purpose for composing this article is to make a straightforward and concrete path for researchers to quickly find the way and the scope in this field for implementing future research on neurological disease detection.
Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field. View Full-Text
Keywords: neurological disorders (NDs); computer-aided diagnosis (CAD); machine learning (ML); deep learning (DL); detection and classification; challenges and opportunities neurological disorders (NDs); computer-aided diagnosis (CAD); machine learning (ML); deep learning (DL); detection and classification; challenges and opportunities
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MDPI and ACS Style

Lima, A.A.; Mridha, M.F.; Das, S.C.; Kabir, M.M.; Islam, M.R.; Watanobe, Y. A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders. Biology 2022, 11, 469. https://doi.org/10.3390/biology11030469

AMA Style

Lima AA, Mridha MF, Das SC, Kabir MM, Islam MR, Watanobe Y. A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders. Biology. 2022; 11(3):469. https://doi.org/10.3390/biology11030469

Chicago/Turabian Style

Lima, Aklima Akter, M. Firoz Mridha, Sujoy Chandra Das, Muhammad Mohsin Kabir, Md. Rashedul Islam, and Yutaka Watanobe. 2022. "A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders" Biology 11, no. 3: 469. https://doi.org/10.3390/biology11030469

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