An intelligent technique for detecting Alzheimer's disease based on brain structural changes and hippocampal shape

Jiji, G. Wiselin; Suji, G. Evelin; Rangini, M.

doi:10.1080/21681163.2013.879838

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…The combination of different types of features can improve the accuracy of the AD diagnosis in comparison to methods which use just a single feature [15]. …”

Section: Introductionmentioning

confidence: 99%

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Xiao

Ding

Lan

et al. 2017

Computational and Mathematical Methods in Medicine

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We propose a novel classification framework to precisely identify individuals with Alzheimer's disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance.

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“…The combination of different types of features can improve the accuracy of the AD diagnosis in comparison to methods which use just a single feature [15]. …”

Section: Introductionmentioning

confidence: 99%

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Xiao

Ding

Lan

et al. 2017

Computational and Mathematical Methods in Medicine

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“…A higher value of sensitivity specifies that there are few false-negative results, and thus fewer cases of the disease are missed. On the other hand, high specificity means that there are few false positive results [14,27]. In the current research, false positives are less important than false negatives because the false-negative answer may lead the patient into a serious condition.…”

Section: Discussionmentioning

confidence: 73%

“…Utilizing the above information, several methods have been suggested to diagnose healthy control (HC), MCI, and AD by using a CAD system. Most studies to the date computed features based on volumetric measurement of segmented region of interest (ROI) [13,14], voxel-based morphometry (VBM) [15,16], and voxel-wise statistical approaches [17,18]. Among statistical approaches, linear discriminant analysis (LDA) [19] and principal component analysis (PCA) [20] are the commonly used statistical tools for feature extraction and data analysis.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

et al. 2021

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Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

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“…The authors employed a random forest, a linear support vector machine, and the k-Nearest Neighbor method to classify the data. G. Wiselin et al [43] offered a new approach to Alzheimer's disease diagnosis based on alterations to brain structural integrity and hippocampal shape. To extract the characteristics, the authors identified the busy texture information.…”

Section: Papermentioning

confidence: 99%

Prediction and Feature Extraction Techniques used for Classification of Alzheimer’s Disease in its Early Stage using MRI: A Review

Merlin,

Pattabiraman

2023

Advances in Computer Science Research

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Most people worldwide are affected by an acute neurological disorder called Alzheimer's disease. Despite the reality that there is no treatment, it can be tapered off if it is detected early enough. 6 million individuals in the United States have Alzheimer's disease, and more than 120,000 people have died as a result of it. This has been designated as the sixth leading cause of death. Early diagnosis of AD can be useful to improve the quality of living of AD patients and will be helpful for their caretakers also. The anatomy of brain is intricate in structure packed with more information which makes it more burdensome to extract the features. This study outlines the machine learning and deep learning techniques utilized in the prediction and categorization of Alzheimer's disease, with an emphasis on the most recent approaches in feature extraction based on texture, voxel, wavelet and graph.

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An intelligent technique for detecting Alzheimer's disease based on brain structural changes and hippocampal shape

Cited by 8 publications

References 24 publications

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Prediction and Feature Extraction Techniques used for Classification of Alzheimer’s Disease in its Early Stage using MRI: A Review

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