Magnetic resonance imaging (MRI)microscopicin Alzheimer diseaseAlzheimer disease (AD)microscopic magnetic resonance imaging inMagnetic Resonance Imaging and Histopathological Correlation in Alzheimer’s Disease

2016 ◽  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Histopathological Correlation ◽  
Magnetic Resonance Imaging Mri

scholarly journals A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

2020 ◽  
Vol 9 (7) ◽  
pp. 2146
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Support Vector ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

scholarly journals Survey on Identification of Alzheimer Disease Using Magnetic Resonance Imaging (MRI) Images

2019 ◽  
Vol 8 (11) ◽  
pp. 3564-3570
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Early Detection ◽  
Low Cost ◽  
Tau Proteins ◽  
Resonance Imaging ◽  
Human Errors ◽  
Magnetic Resonance Imaging Mri

Alzheimer’s disease (AD) is a neuro-degenerative disorder which is characterised functional and cognitive deficits that take place progressively. Early detection of the AD is important for the therapy to be early and this may slow down the disease and its progression. For the purpose of bringing about an improvement to the incidence of early detection of the AD, there may be certain Normal Controls (NC) that is based on the structural analysis of Magnetic Resonance Imaging (MRI). In fact, an early detection of the AD by means of using an MRI can help both patients, as well as physicians, to a great extent since it is of a low cost and is also a procedure that is non-invasive providing objective diagnosis by avoiding human errors. This has been connected to the accumulation of the amyloid and the tau proteins found in the brain which is probably the commonest cause for a case of dementia and also accounts for almost 70% of cases of dementia. The MRI is an extremely promising technique in terms of detection of functional or structural brain differences observed among both these patient populations. For the purpose of this work, there had been a new survey that had been made for the identification of a new case of Alzheimer’s disease by means of using the MRI images.

Prevalence of Sinusitis Detected by Magnetic Resonance Imaging in Subjects with Dementia or Alzheimer’s Disease

2015 ◽  
Vol 12 (10) ◽  
pp. 1006-1011 ◽  
Author(s):  
Minori Yasue ◽  
Saiko Sugiura ◽  
Yasue Uchida ◽  
Hironao Otake ◽  
Masaaki Teranishi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Resonance Imaging
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