scholarly journals Convolutional Neural Network Based Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Technique using Hippocampus Extracted from MRI

2020 ◽  
Vol 20 (2) ◽  
pp. 113-122
Author(s):  
G. MUKHTAR ◽  
S. FARHAN
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Convolutional Neural Network

scholarly journals Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7634
Author(s):  
Peng Zhang ◽  
Shukuan Lin ◽  
Jianzhong Qiao ◽  
Yue Tu
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Memory Loss ◽  
Main Tool ◽  
Disease Diagnosis

Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.

scholarly journals Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kanghan Oh ◽  
Young-Chul Chung ◽  
Ko Woon Kim ◽  
Woo-Sung Kim ◽  
Il-Seok Oh
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Classification Task ◽  
Classification Tasks ◽  
End To End

AbstractRecently, deep-learning-based approaches have been proposed for the classification of neuroimaging data related to Alzheimer’s disease (AD), and significant progress has been made. However, end-to-end learning that is capable of maximizing the impact of deep learning has yet to receive much attention due to the endemic challenge of neuroimaging caused by the scarcity of data. Thus, this study presents an approach meant to encourage the end-to-end learning of a volumetric convolutional neural network (CNN) model for four binary classification tasks (AD vs. normal control (NC), progressive mild cognitive impairment (pMCI) vs. NC, stable mild cognitive impairment (sMCI) vs. NC and pMCI vs. sMCI) based on magnetic resonance imaging (MRI) and visualizes its outcomes in terms of the decision of the CNNs without any human intervention. In the proposed approach, we use convolutional autoencoder (CAE)-based unsupervised learning for the AD vs. NC classification task, and supervised transfer learning is applied to solve the pMCI vs. sMCI classification task. To detect the most important biomarkers related to AD and pMCI, a gradient-based visualization method that approximates the spatial influence of the CNN model’s decision was applied. To validate the contributions of this study, we conducted experiments on the ADNI database, and the results demonstrated that the proposed approach achieved the accuracies of 86.60% and 73.95% for the AD and pMCI classification tasks respectively, outperforming other network models. In the visualization results, the temporal and parietal lobes were identified as key regions for classification.

Detection of Alzheimer’s Disease Versus Mild Cognitive Impairment Using a New Modular Hybrid Neural Network

2021 ◽  
pp. 223-235
Author(s):  
Alberto Sosa-Marrero ◽  
Ylermi Cabrera-León ◽  
Pablo Fernández-López ◽  
Patricio García-Báez ◽  
Juan Luis Navarro-Mesa ◽  
...  
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Hybrid Neural Network

scholarly journals Diagnosis of Alzheimer’s Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Morteza Amini ◽  
MirMohsen Pedram ◽  
AliReza Moradi ◽  
Mahshad Ouchani
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Convolutional Neural Network ◽  
Nearest Neighbors ◽  
Time Dependent ◽  
Support Vector ◽  
Linear Discriminant

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer’s disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer’s disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k -nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer’s disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer’s disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k -nearest neighbors’ approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.

scholarly journals Implementation of an Artificial Neural Network on the Test Barcelona Workstation As a Predictive Model for the Classification of Normal, Mild Cognitive Impairment and Alzheimer’s Disease Subjects Using the Neuronorma Battery

2018 ◽  
Author(s):  
N Rivera ◽  
M Cabrera-Bean ◽  
G Sánchez-Benavides ◽  
C Gallego-González ◽  
J A Lupiáñez-Pretel ◽  
...  
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Artificial Neural Network ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Multiple Discriminant Analysis ◽  
Artificial Neural

Objective: To develop and implement an online Artificial Neural Network (ANN) that provides the probability of a subject having mild cognitive impairment (MCI) or Alzheimer’s disease (AD). Method: Different ANNs were trained using a sample of 350 controls (CONT), 75 MCI and 93 AD subjects. The ANN structure chosen was the following: (1) an input layer of 33 cognitive variables from the Neuronorma battery plus two sociodemographic variables, age and education. This layer was reduced to a 15 features input vector using Multiple Discriminant Analysis method, (2) one hidden layer with 8 neurons, and (3) three output neurons corresponding to the 3 expected cognitive states. This ANN was defined in a previous study [28]. The ANN was implemented on the web site www.test-barcelona.com (Test Barcelona Workstation) [9]. Results: When comparing CONT, MCI and AD participants, the best ANN correctly classifies up to 94,87% of the study participants. Conclusions: The online implemented ANN, delivers the probabilities (%) of belonging to the CONT, MCI and AD groups of a subject assessed using the 35 characteristics (variables) of the Neuronorma profile. This tool is a good complement for the interpretation of cognitive profiles. This technology improves clinical decision making. Keywords: Artificial Neural Network, Probability, Alzheimer disease, Test Barcelona Workstation.

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