Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer’s disease diagnosis

2021 ◽  
Vol 66 ◽  
pp. 102397
Author(s):  
Rohollah Hedayati ◽  
Mohammad Khedmati ◽  
Mehran Taghipour-Gorjikolaie
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Extraction Method ◽  
Disease Diagnosis ◽  
Feature Extraction Method ◽  
Deep Feature ◽  
Deep Feature Extraction ◽  
Alzheimer’S Disease Diagnosis

scholarly journals Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment

2019 ◽  
Vol 12 ◽  
pp. 175628641983868 ◽  
Author(s):  
Yupeng Li ◽  
Jiehui Jiang ◽  
Jiaying Lu ◽  
Juanjuan Jiang ◽  
Huiwei Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Cognitive Impairment ◽  
Fdg Pet ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. Methods: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student’s t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach’s alpha coefficient for radiomic feature stability analyses. Pearson’s correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer’s disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Results: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. Conclusion: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.

scholarly journals Hierarchical Feature Extraction for Early Alzheimer’s Disease Diagnosis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 93752-93760 ◽  
Author(s):  
Lulu Yue ◽  
Xiaoliang Gong ◽  
Jie Li ◽  
Hongfei Ji ◽  
Maozhen Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Disease Diagnosis ◽  
Early Alzheimer’S Disease ◽  
Alzheimer’S Disease Diagnosis

scholarly journals An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fengtao Wang ◽  
Bosen Dun ◽  
Xiaofei Liu ◽  
Yuhang Xue ◽  
Hongkun Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Kernel Function ◽  
Extraction Method ◽  
Operating Conditions ◽  
Model Parameters ◽  
Layer By Layer ◽  
Feature Extraction Method ◽  
Deep Feature ◽  
Deep Feature Extraction

Rotating machinery vibration signals are nonstationary and nonlinear under complicated operating conditions. It is meaningful to extract optimal features from raw signal and provide accurate fault diagnosis results. In order to resolve the nonlinear problem, an enhancement deep feature extraction method based on Gaussian radial basis kernel function and autoencoder (AE) is proposed. Firstly, kernel function is employed to enhance the feature learning capability, and a new AE is designed termed kernel AE (KAE). Subsequently, a deep neural network is constructed with one KAE and multiple AEs to extract inherent features layer by layer. Finally, softmax is adopted as the classifier to accurately identify different bearing faults, and error backpropagation algorithm is used to fine-tune the model parameters. Aircraft engine intershaft bearing vibration data are used to verify the method. The results confirm that the proposed method has a better feature extraction capability, requires fewer iterations, and has a higher accuracy than standard methods using a stacked AE.

scholarly journals Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hancan Zhu ◽  
Zhenyu Tang ◽  
Hewei Cheng ◽  
Yihong Wu ◽  
Yong Fan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Manual Segmentation ◽  
Feature Extraction Method ◽  
Label Fusion ◽  
Hippocampus Segmentation

AbstractAutomatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies. To improve the hippocampus segmentation performance, a local binary pattern based feature extraction method is developed for machine learning based multi-atlas hippocampus segmentation. Under the framework of multi-atlas image segmentation (MAIS), a set of selected atlases are registered to images to be segmented using a non-linear image registration algorithm. The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted using a novel image feature extraction method. The RLBP based MAIS algorithm has been validated for segmenting hippocampus based on a data set of 135 T1 MR images which are from the Alzheimer’s Disease Neuroimaging Initiative database (adni.loni.usc.edu). By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. We further computed Cohen’s d effect size to investigate the sensitivity of each segmenting method in detecting volumetric differences of the hippocampus between different groups of subjects. The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. Hippocampal volumetric analysis showed that the proposed RLBP method performed well in detecting the volumetric differences of the hippocampus between groups of Alzheimer’s disease patients, mild cognitive impairment subjects, and normal controls. These results have demonstrated that the RLBP based multi-atlas image segmentation method could facilitate efficient and accurate extraction of the hippocampus and may help predict Alzheimer’s disease. The codes of the proposed method is available (https://www.nitrc.org/frs/?group_id=1242).

Model Based on Deep Feature Extraction for Diagnosis of Alzheimer’s Disease

2019 ◽  
Author(s):  
Iago R. R. Silva ◽  
Gabriela S. L. Silva ◽  
Rodrigo G. de Souza ◽  
Wellington P. dos Santos ◽  
Roberta A. de A. Fagundes
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Model Based ◽  
Deep Feature ◽  
Deep Feature Extraction
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