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234 Computational Intelligence Algorithms
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Deep Learning
16
Techniques in Neurological Disorder Detection
Manisha Nagar, Shikha Singh, Sanjay Singh, and Ruchi Jain
16.1 INTRODUCTION
Neurological problems affect people of all ages. The prevalence of these disorders has signicantly risen over the past four to ve years. In numerous instances, there are no detectable tools for diagnosing neurological disorders. A primary cause of these disorders is electrical abnormalities in the brain. Today, various neurological disorders can be diagnosed using advanced technologies such as electroencepha­logram (EEG), magnetic resonance imaging (MRI), computed tomography (CT) scans, and positron emission tomography (PET) scans. In previous years, machine learning (ML) algorithms were primarily used to analyze neuroimaging data when datasets were small. However, with larger datasets, deep learning (DL) has become necessa ry.
Parkinson’s disease (PD), schizophrenia (SZ), and Alzheimer’s disease (AD) are three prevalent neurological conditions [1]. AD is observed by increasing men- tal decline; it usually affects elderly persons as a result of specic brain regions deteriorating. Extensive research has been undertaken to accurately identify the causes of this degeneration and develop automated methods for detecting degenera­tion patterns in neuroimages. It ranks as the fourth leading contributor to mortality worldwide, following heart disease, cancer, and brain hemorrhage. AD has three states: very mildly demented, mildly demented, and moderately demented [2] (see
Figures 16.1–16.3).
In the very mildly demented stage, patients begin to forget where they have kept their belongings and may have difculty remembering recently learned names. In the mildly demented stage, patients have difculty remembering words, often get lost even on familiar routes, and show a decrease in focus and work abilities. In the moderately demented stage, they begin to forget recent activities and signicant past events, struggle with budgeting, nd it difcult to go outside alone, and experience a loss of empathy [3].
Tremors, bradykinesia, stiffness, and unstable posture are among the motor signs of PD disease, a neurodegenerative condition brought on by the loss of
DO I: 10.1201/ 97810 03520 34 4 -19
239
240 Computational Intelligence Algorithms
FIGURE 16.1 MRI of a mildly demented patient.
FIGURE 16.2 Moderately demented.
241 Deep Learning Techniques in Neurological Disorder Detection
FIGURE 16.3 Very mildly demented.
dopaminergic activity. PD is the second foremost neurological disease affecting older persons [4]. The exact cause of the disease remains unknown. PD has high rates of mortality and requires early diagnosis and proper treatment to allevi­ate personal, social, and national burdens. Two imaging techniques commonly utilized to detection are PET and single photon emission computed tomography (SPECT). Table 16.1 presents an overview of the procedures followed in recent research that employed statistical and ML methods to forecast the presence of PD from MRI data.
Schizophrenia is a chronic mental condition that impacts around 1% of the population globally. According to a World Health Organization (WHO) report, around 24 million individuals around the world, or roughly one in every 300, are affected by schizophrenia (0.32%). The incidence is higher among adults, at one in 222 (0.45%). Schizophrenia is not very common compared to several other mental diseases. It typically begins in adulthood or in the 20s, usually men suf­fering it sooner than women. Some schizophrenia symptoms may be explained by difculties with the neurological system’s corollary discharge process, which may make it difcult for patients to distinguish between internally and externally produced feelings. The exact cause of schizophrenia remains unknown, but fac­tors such as stressful life events, drug use, and their combinations have been proposed to have contributed to its growth. Neuroimaging is crucial for reveal­ing both functional and structural changes in the human brain. Individuals with
242 Computational Intelligence Algorithms
TABLE 16.1 An Overview of the Procedures Followed in Recent Research that Employed Statistical and ML Methods to Forecast the Presence of PD from MRI Data
Reference Input Data Active Method Accuracy (%)
[5] PD (57) Voxel-based morphometry (VBM), 100
diffusion tensor imaging (DTI) [6] PSP (21) Support vector machine (SVM) [7] PD (27) Functional connectome 80
HC (38) SVM HC (26)
[8] PPMI cohort Connectivity measures 93
PD (374) SVM HC (169)
[9] PD (30) Region of interest based 86.67
HC (30) SVM
schizophrenia often face human rights breaches in both treatment facilities and the community. People with this illness experience social exclusion, and relation­ships with family and friends suffer as a result of the solid and pervasive stigma against them. Due to discrimination brought on by this stigma, they may have fewer options for housing, work, education, and general healthcare. Structural MRI of brain anatomy offers a reliable method for diagnosing schizophrenia. In the domain of medical imaging, convolutional neural networks (CNNs) have shown to be benecial instruments for the automated diagnosis of a variety of neurological disorders, including schizophrenia. Millions of people worldwide suffer from schizophrenia, which has a major negative impact on both individuals and society. Early and correct diagnosis is critical to effective treatment and man­agement. However, clinical evaluations and manual brain scan interpretation are signicant components of traditional diagnostic techniques, which can be incon­sistent and error-prone. The development of CNNs, which use DL to recognize complex patterns in MRI images that may be suggestive of schizophrenia, has made a potent substitute available.
The motivation of this chapter is to address the limitations of traditional MRI analysis in diagnosing neurological disorders and to explore how DL can revolution­ize this eld. Conventional approaches often struggle with accuracy, scalability, and efciency, leading to unreliable diagnostic results.
The objective of this chapter is achieved through the following subtasks:
i. Compare DL methods with traditional MRI analysis for diagnosing schizo-
phrenia, PD, and AD.
ii. Outline each disorder’s data preprocessing steps and algorithm choices.
iii. Explore performance analysis of DL in neurological diagnosis and identify
challenges in extending these methods to new conditions.
243 Deep Learning Techniques in Neurological Disorder Detection
16.2 RELATED RESEARCH
Zhang et al. [10] demonstrated a DL-based strategy for identifying the difference between healthy brains and those with AD. Because AD affects many people, there has been much interest in detecting the condition using MRI and DL. CNNs have been uti­lized in several works to categorize various AD phases and distinguish them from mild cognitive impairment (MCI) and t persons. The use of MRI and DL for AD detection has become a primary research focus due to the disease’s prevalence and impact. Suk et al. [11] utilized a DL model combining sparse autoencoders and a deep belief network to extract features from MRI images, achieving signicant accuracy improvements in AD classication. Payan and Montana [12] developed a 3D CNN to process volumetric MRI data, demonstrating superior performance in detecting early AD stages compared to traditional ML methods. Liu et al. [13] used a multimodal approach integrating MRI with PET data, using a DL framework to enhance the diagnostic accuracy of AD.
In [14], the authors applied a CNN to structural MRI scans, focusing on the sub­stantia nigra region, which is critical in PD pathology. Their model obtained sig­nicant accuracy as well as specicity in identifying PD patients from t persons. Pereira et al. [15] used resting-state functional MRI (fMRI) data, CNNs could dis­tinguish PD patients and healthy controls, yielding encouraging ndings. MRI-based DL has showed promise in detecting and diagnosing schizophrenia, a complicated psychiatric condition with various symptoms. Vieira et al. [16] created a deep neural network (DNN) to examine brain connection patterns in fMRI data. Their approach distinguished between patients with and without schizophrenia.
16.3 SUMMARY OF DL TECHNIQUES
Recent developments in neuroimaging modalities, including PET, magnetoencepha­lography (MEG), and MRI, have improved our knowledge of how the brain functions. Numerous machine and DL approaches, along with high-performance computer tools, have made diagnosing and classifying neurological diseases possible. PD has been identied in MRI scans using various ML approaches, such as SVM, articial neural networks (ANN), decision tree (DT) models, and Bayes algorithms. The symptoms of PD frequently begin on one side of the body and may be related to asymmetries in the brain’s cortical or subcortical systems [17]. Table 16.2 summarizes recent research employing MRI techniques to predict PD using ML methodologies. Supervised learn­ing approaches are practical for ML applications such as regression, classication, pattern recognition, and feature extraction. Neurological problems involve the entire body, including the brain, spinal cord, and nerves, according to scientic classica­tions. Three prevalent neurological ailments include PD, AD, and mental disorders, such as schizophrenia. In this context, computational intelligence algorithms, particu­larly DL techniques, have emerged as powerful tools for automating the analysis of medical images and improving the accuracy of neurodisorder diagnosis. Currently, various DL approaches are being used to study and cure neurodiseases. These include neural networks such as ANN, DNN, Autoencoder (AE), CNN, probabilistic neural networks (PNN), K-nearest neighbors (K-NN), recurrent neural networks (RNN), and long short-term memory (LSTM).
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