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

Abstract

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.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

“…It can be seen from Table 1 and Table 2 that eight studies produced results of 100% for at least one measure. MRI is one of the most widely used diagnosis methods for neurological diseases because it generates accurate and fast results, and it is a secure and non-invasive procedure [ 107 ]. However, it is worth mentioning that among the studies that produced 100% results, 5 of the studies used MRI, while the other studies used different datatypes like OCT, ERG, and clinical features.…”

Automatic Sample Segmentation & Detection of Parkinson’s Disease Using Synthetic Staining & Deep Learning

“…Parkinson's UK Brain Bank, at Imperial College London, has produced a dataset containing digitised images of brain sections immunostained for the protein alpha-synuclein (𝛼-syn), the pathological marker of PD; along with control cases from healthy donors. This dataset is much larger (over 400 cases), more consistent, and of higher quality (all have been stained with the same protocol and imaged within the same laboratory) than has been documented elsewhere in the literature; including those found in a meta-analysis study on detection of neurological disorders containing over 200 papers (Lima et al, 2022).…”

“…Accurate classification and stratification of PD is critical for the confirmation that the brain donor suffered from PD and to maximise the potential usefulness of the brain in research studies to better understand the causes of PD and foster drug development. Parkinson’s UK Brain Bank, at Imperial College London, has produced a dataset containing digitised images of brain sections immunostained for the protein alpha-synuclein ( α -syn), the pathological marker of PD; along with control cases from healthy donors. This dataset is much larger (over 400 cases), more consistent, and of higher quality (all have been stained with the same protocol and imaged within the same laboratory) than has been documented elsewhere in the literature; including those found in a meta-analysis study on detection of neurological disorders containing over 200 papers (Lima et al, 2022). The project team, consisting of neuroscientists and subject matter experts from: Imperial, NHS AI Lab Skunkworks, Parkinson’s UK, and Polygeist have undertaken a 12 week project to examine the possibility of producing a Proof-of-Concept (PoC) tool to automatically load, enhance and ultimately classify those brain sections containing α -syn.…”

Diagnostic accuracy study of automated stratification of Alzheimer’s disease and mild cognitive impairment via deep learning based on MRI

“…To date, researchers have developed multiple computeraided systems to establish a precise disease diagnosis (13). Between the 1970s and 1990s, scientists designed a rulebased expert framework, and post 1990s, they designed supervised models.…”