Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Meng, Xianglian; Wu, Yue; Liu, Wenjie; Wang, Ying; Xu, Zhe; Jiao, Zhuqing

doi:10.3389/fninf.2022.856295

Cited by 6 publications

(5 citation statements)

References 62 publications

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“…Consistent with the results in Reference [27], the amygdala has been identified as an important brain region, and our proposed method achieved an accuracy of 93.9% after leave-one-out validation. The amygdala played an important role in AD [28][29][30].…”

Section: Discussionsupporting

confidence: 86%

“…Then, we selected the top genes and brain regions with a frequency above two to count their relation. The results are shown in Figure 12 Consistent with the results in Reference [27], the amygdala has been identified as an important brain region, and our proposed method achieved an accuracy of 93.9% after leave-one-out validation. The amygdala played an important role in AD [28][29][30].…”

Section: Discussionsupporting

confidence: 83%

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Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model

Hu,

Tang,

Liang

et al. 2024

Mathematics

Self Cite

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Alzheimer’s disease (AD) is a significant neurological disorder characterized by progressive cognitive decline and memory loss. One essential task is understanding the molecular mechanisms underlying brain disorders of AD. Detecting biomarkers that contribute significantly to the classification of AD is an effective means to accomplish this essential task. However, most machine learning methods used to detect AD biomarkers require lengthy training and are unable to rapidly and effectively detect AD biomarkers. To detect biomarkers for AD accurately and efficiently, we proposed a novel approach using the Multi-Kernel Support Vector Machine (SVM) with Apriori algorithm to mine strongly associated feature sets from functional magnetic resonance imaging (fMRI) and gene expression profiles. Firstly, we downloaded the imaging data and genetic data of 121 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and transformed gene sequences into labeled sequences by encoding the four types of bases (A, T, C, and G) into distinct labels. Subsequently, we extracted the first 130 temporal sequences of brain regions and employed Pearson correlation analysis to construct “brain region gene pairs”. The integration of these data allowed us to explore the correlations between genes and brain regions. To improve classification accuracy and feature selection, we applied the Apriori algorithm to the multi-kernel SVM, dynamically building feature combinations and continuously validating classification results. By iteratively generating frequent itemsets, we obtained important brain region gene pairs. Experimental results show the effectiveness of our proposed approach. The Multi-Kernel SVM with Apriori model achieves an accuracy of 92.9%, precision of 95%, and an F1 score of 95% in classifying brain region-gene pairs within the AD–Late mild cognitive impairment (AD-LMCI) group. The amygdala, BIN1, RPN2, and IL15 associated with AD have been identified and demonstrate potential in identifying potential pathogenic factors of AD. The selected brain regions and associated genes may serve as valuable biomarkers for early AD diagnosis and better understanding of the disease’s molecular mechanisms. The integration of fMRI and gene data using the Multi-Kernel SVM–Apriori model holds great potential for advancing our knowledge of brain function and the genetic basis of neurological disorders. This approach provides a valuable tool for neuroscientists and researchers in the field of genomics and brain imaging studies.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionsupporting

confidence: 83%

Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model

Hu,

Tang,

Liang

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

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“…Recently, researchers have focused on two main approaches for training and classifying models, conventional learning and deep learning, as they proved excellent in other domains [ 28 , 29 , 30 ]. Recent studies like [ 24 , 31 , 32 , 33 , 34 ] used conventional learning for AD classification, while studies like [ 5 , 35 , 36 , 37 , 38 ] employed deep learning techniques.…”

Section: Related Workmentioning

confidence: 99%

“…During the training and testing stage for early AD detection, EfficientNetB0, EfficientNetB2, and EfficientNetB3 achieved an accuracy of 92.20%, 94.42%, and 97.28%, respectively. Meng et al [ 32 ] employ a model for AD diagnosis to perform the voxel-based morphometry (VBM) data obtained from MRI scans consisting of 1426 MRIs. They extracted the features based on a random survey Support Vector Machine (RS-SVM) approach.…”

Section: Related Workmentioning

confidence: 99%

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Hassan,

Musa Miah,

Shin

2024

J. Imaging

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Alzheimer’s Disease (AD) poses a significant health risk globally, particularly among the elderly population. Recent studies underscore its prevalence, with over 50% of elderly Japanese facing a lifetime risk of dementia, primarily attributed to AD. As the most prevalent form of dementia, AD gradually erodes brain cells, leading to severe neurological decline. In this scenario, it is important to develop an automatic AD-detection system, and many researchers have been working to develop an AD-detection system by taking advantage of the advancement of deep learning (DL) techniques, which have shown promising results in various domains, including medical image analysis. However, existing approaches for AD detection often suffer from limited performance due to the complexities associated with training hierarchical convolutional neural networks (CNNs). In this paper, we introduce a novel multi-stage deep neural network architecture based on residual functions to address the limitations of existing AD-detection approaches. Inspired by the success of residual networks (ResNets) in image-classification tasks, our proposed system comprises five stages, each explicitly formulated to enhance feature effectiveness while maintaining model depth. Following feature extraction, a deep learning-based feature-selection module is applied to mitigate overfitting, incorporating batch normalization, dropout and fully connected layers. Subsequently, machine learning (ML)-based classification algorithms, including Support Vector Machines (SVM), Random Forest (RF) and SoftMax, are employed for classification tasks. Comprehensive evaluations conducted on three benchmark datasets, namely ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS Kaggle, demonstrate the efficacy of our proposed model. Impressively, our model achieves accuracy rates of 99.47%, 99.10% and 99.70% for ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS datasets, respectively, outperforming existing systems in binary class problems. Our proposed model represents a significant advancement in the AD-analysis domain.

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“…The extraction of grey matter, also known as tissue segmentation, is performed on MRI images. In [15], 649 voxel-based morphometry (VBM) images were used to come up with a new framework for voxel-based feature detection. The limitations of ROI-based methods were eased in this method, but the effects of high-dimensional data were still strong.…”

Section: Voxel-based Feature Extractionmentioning

confidence: 99%

Alzheimer’s Disease Diagnosis using Machine Learning: A Review

Balakrishnan¹,

Sreeja²,

Panackal³

2022

IJETT

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Alzheimer's Disease (AD) is an acute neuro disease that degenerates the brain cells and thus leads to memory loss progressively. It is a fatal brain disease that mostly affects the elderly. It steers the decline of cognitive and biological functions of the brain and shrinks the brain successively, which in turn is known as Atrophy. For an accurate diagnosis of Alzheimer's disease, cutting-edge methods like machine learning are essential. Recently, machine learning has gained a lot of attention and popularity in the medical industry. As the illness progresses, those with Alzheimer's have a far more difficult time doing even the most basic tasks, and in the worst case, their brain completely stops functioning. A person's likelihood of having early-stage Alzheimer's disease may be determined using the ML method. In this analysis, papers on Alzheimer's disease diagnosis based on deep learning techniques and reinforcement learning between 2008 and 2023 found in google scholar were studied-sixty relevant papers obtained after the search was considered for this study. These papers were analysed based on the biomarkers of AD and the machine-learning techniques used. The analysis shows that deep learning methods have an immense ability to extract features and classify AD with good accuracy. The DRL methods have not been used much in the field of image processing. The comparison results of deep learning and reinforcement learning illustrate that the scope of Deep Reinforcement Learning (DRL) in dementia detection needs to be explored.

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Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Cited by 6 publications

References 62 publications

Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model

Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Alzheimer’s Disease Diagnosis using Machine Learning: A Review

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