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 Autophagy and mitophagy biomarkers are reduced in sera of patients with Alzheimer’s disease and mild cognitive impairment

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Massimiliano Castellazzi ◽  
Simone Patergnani ◽  
Mariapina Donadio ◽  
Carlotta Giorgi ◽  
Massimo Bonora ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Late Onset ◽  
Memory Loss ◽  
Serum Levels ◽  
Enzyme Linked Immunosorbent Assay ◽  
Serum Samples ◽  
Cellular Processes

AbstractDementia is a neurocognitive disorder characterized by a progressive memory loss and impairment in cognitive and functional abilities. Autophagy and mitophagy are two important cellular processes by which the damaged intracellular components are degraded by lysosomes. To investigate the contribution of autophagy and mitophagy in degenerative diseases, we investigated the serum levels of specific autophagic markers (ATG5 protein) and mitophagic markers (Parkin protein) in a population of older patients by enzyme-linked immunosorbent assay. Two hundred elderly (≥65 years) outpatients were included in the study: 40 (20 F and 20 M) with mild-moderate late onset Alzheimer’s disease (AD); 40 (20 F and 20 M) affected by vascular dementia (VAD); 40 with mild cognitive impairment (MCI); 40 (20 F and 20 M) with “mixed” dementia (MD); 40 subjects without signs of cognitive impairment were included as sex-matched controls. Our data indicated that, in serum samples, ATG5 and Parkin were both elevated in controls, and that VAD compared with AD, MCI and MD (all p < 0.01). Patients affected by AD, MD, and MCI showed significantly reduced circulating levels of both ATG5 and Parkin compared to healthy controls and VAD individuals, reflecting a significant down-regulation of autophagy and mitophagy pathways in these groups of patients. The measurement of serum levels of ATG5 and Parkin may represent an easily accessible diagnostic tool for the early monitoring of patients with cognitive decline.

scholarly journals Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG

2021 ◽  
Author(s):  
Matous Cejnek ◽  
Oldrich Vysata ◽  
Martin Valis ◽  
Ivo Bukovsky
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Novelty Detection ◽  
Disease Diagnosis ◽  
Eeg Signal ◽  
New Approach ◽  
Activity Assessment ◽  
Adaptive Filtration

AbstractAlzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.

scholarly journals Early Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture

2021 ◽  
Vol 8 (2) ◽  
pp. 48-57
Author(s):  
Deepthi Kamath ◽  
Misba Firdose Fathima ◽  
Monica K. P ◽  
Kusuma Mohanchandra
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Brain Mri ◽  
Memory Loss ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Mri Scans

Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s  Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.

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