Lima Aklima Akter, Mridha M Firoz, Das Sujoy Chandra, Kabir Muhammad Mohsin, Islam Md Rashedul, Watanobe Yutaka
Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh.
Biology (Basel). 2022 Mar 18;11(3):469. doi: 10.3390/biology11030469.
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.
神经系统疾病（NDs）正变得越来越普遍，这引起了孕妇、父母、健康婴儿和儿童的关注。神经系统疾病有多种形式，每种形式都有其自身的起源、并发症和后果。近年来，由于神经成像技术，如磁共振成像（MRI）、脑磁图（MEG）和正电子发射断层扫描（PET）等，人们对大脑功能的复杂性有了更好的理解。借助高性能计算工具以及各种机器学习（ML）和深度学习（DL）方法，这些技术在识别和诊断神经系统疾病方面发现了令人兴奋的可能性。本研究遵循计算机辅助诊断方法，对预处理和特征提取技术进行了概述。本文对现有的用于检测神经系统疾病的ML和DL方法的性能进行了批判性回顾和比较。本研究的一个全面部分还展示了各种检测和记录图像、信号及语音等的技术和特定疾病数据集。由于该领域专注于疾病和检测标准的研究工作明显较少，因此也总结了有限的相关NDs研究。本研究还介绍了一些标准评估指标，以便进行更好的结果分析和比较。这项研究还按照一致的工作流程进行了概述。最后，包含了一个强制性的讨论部分，以阐述这一新兴领域中开放的研究挑战和未来工作的方向。
