Deep Learning for Alzheimer’s Disease Classification using Texture Features

So, Jae-Hong; Madusanka, Nuwan; Choi, Heung‐Kook; Choi, Boo-Kyeong; Park, Hyeon-Gyun

doi:10.2174/1573405615666190404163233

Cited by 27 publications

(8 citation statements)

References 43 publications

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“…In a study of circulating non-coding RNA in patients with AD, 21 disease-related features were identified using RT-qPCR, and 18 strongly correlated features were extracted using statistical learning methods to establish a machine learning model, with an AUC of about 0.86 ( Herrero-Labrador et al, 2020 ). In an AD classifier based on texture features, the researchers modeled the high-level semantic features of MRI with an accuracy of about 85% ( So et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease

Liu

Jian-hu

et al. 2021

Front. Aging Neurosci.

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Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer’s disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to study the changes of blood transcriptome during the development of AD and to search for potential blood biomarkers for AD.Methods: Individualized blood mRNA expression data of 711 patients were downloaded from the GEO database, including the control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, the XCell tool was used to analyze the blood mRNA expression data and obtain blood cell composition and quantitative data. Ratio characteristics were established for mRNA and XCell data. Feature engineering operations such as collinearity and importance analysis were performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set.Results: Through feature engineering screening, the best feature collection was obtained. Moreover, the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The area under curve (AUC) of CON, MCI, and AD were 0.9746, 0.9536, and 0.9807, respectively.Conclusion: The results of cell homeostasis analysis suggested that the homeostasis of Natural killer T cell (NKT) might be related to AD, and the homeostasis of Granulocyte macrophage progenitor (GMP) might be one of the reasons for AD.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease

Liu

Jian-hu

et al. 2021

Front. Aging Neurosci.

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“…Voice data were collected, pre-processed, normalized, and converted into a spectrogram to obtain MFCC images. Deep learning models are commonly trained with the MFCC transition to utilize the advantages of sound signals and non-verbal elements [ 71 , 72 , 73 , 74 ]. MFCC features have been widely used in voice classification tasks, as they have been shown to perform well in terms of robustness and discrimination power.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning of Speech Data for Early Detection of Alzheimer’s Disease in the Elderly

Ahn,

Cho,

Kim

et al. 2023

Bioengineering

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Background: Alzheimer’s disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment. Objective: Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics. Materials and Methods: The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results. Results: Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group. Conclusions: The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection.

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“…Multivariate approaches including random forest, bootstrapping, and k-nearest neighbours (k-NNs) showed significant changes in hippocampal texture. So et al 13 examined texture patterns in the hippocampus in order to differentiate HN, MCI, and AD. They extracted gray-level co-occurrence matrix (GLCM) features and used a multi-layer perceptron.…”

Section: Related Workmentioning

confidence: 99%

Changes in 3D radiomic texture descriptors in Alzheimer’s disease stages

Duarte

Barros²,

Gobbi³

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

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Alzheimer's disease (AD) is a neurodegenerative disease that induces cognitive and functional decline in the elderly. Detecting AD in its early, pre-dementia stages (i.e., during mild cognitive impairment, MCI) is challenging as the rate of atrophy varies among brain regions. This progressive degeneration, nevertheless, potentially can be identified in brain imaging by examining changes in texture descriptors. Textures in images are thought to reveal underlying patterns of structural change in brain tissue. In addition, careful examination of a panel of texture descriptors may allow for improved prediction of disease stages. This study evaluates four common groups of texture descriptors (GLCM-gray-level co-occurrence matrix, GLRLM-gray-level run-length matrix, GLSZMgray-level size zone matrix, and NGTDM-neighbouring gray tone difference matrix) across 85 anatomical brain regions in three clinical populations (HN -healthy normal, MCI, and AD). Specifically, using statistical hypothesis validation at the regional level, differences in texture descriptors were analyzed in three comparative scenarios (HN vs MCI, HN vs AD, and MCI vs AD) and multiple comparisons were corrected using false discovery rate (FDR). From sixty-one examined texture descriptors, only six (9.8%) were found to be statistically significant (p < 0.05) in all three comparative scenarios after FDR correction -five were GLRLM features and one was GLCM. Our results highlighted significant changes in texture descriptors among HN, MCI, and AD populations, and they align with existing biological and computational findings in the literature.

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Deep Learning for Alzheimer’s Disease Classification using Texture Features

Cited by 27 publications

References 43 publications

Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease

Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease

Deep Learning of Speech Data for Early Detection of Alzheimer’s Disease in the Elderly

Changes in 3D radiomic texture descriptors in Alzheimer’s disease stages

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