A new scheme for the automatic assessment of Alzheimer’s disease on a fine motor task with Transfer Learning*

2021 ◽  
Author(s):  
M. Kachouri ◽  
N. Houmani ◽  
S. Garcia-Salicetti ◽  
A.-S. Rigaud
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transfer Learning ◽  
Motor Task ◽  
Fine Motor ◽  
Automatic Assessment

Deep Transfer Learning for Alzheimer's disease detection

2021 ◽  
Author(s):  
Nicole Dalia Cilia ◽  
Claudio De Stefano ◽  
Claudio Marrocco ◽  
Francesco Fontanella ◽  
Mario Molinara ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transfer Learning ◽  
Disease Detection

A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images

Neuroscience ◽  
2021 ◽  
Author(s):  
Atif Mehmood ◽  
Shuyuan yang ◽  
Zhixi feng ◽  
Min wang ◽  
AL Smadi Ahmad ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Transfer Learning ◽  
Learning Approach

The effects of a concurrent motor task on walking in Alzheimer's disease

2014 ◽  
Vol 39 (1) ◽  
pp. 291-296 ◽  
Author(s):  
Joanne E. Wittwer ◽  
Kate E. Webster ◽  
Keith Hill
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Motor Task

scholarly journals A Comparative Study of Alzheimer’s Disease Classification using Multiple Transfer Learning Models

2019 ◽  
Vol 6 (4) ◽  
pp. 209-216
Author(s):  
Deekshitha Prakash ◽  
Nuwan Madusanka ◽  
Subrata Bhattacharjee ◽  
Hyeon-Gyun Park ◽  
Cho-Hee Kim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Comparative Study ◽  
Transfer Learning ◽  
Disease Classification ◽  
Learning Models

scholarly journals Association between motor task acquisition and hippocampal atrophy across cognitively unimpaired, amnestic Mild Cognitive Impairment, and Alzheimer's disease individuals

2021 ◽  
Author(s):  
Sydney Y Schaefer ◽  
Michael Malek-Ahmadi ◽  
Andrew Hooyman ◽  
Jace B. King ◽  
Kevin Duff
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Sex Education ◽  
Low Cost ◽  
Motor Task ◽  
Amnestic Mild Cognitive Impairment ◽  
Hippocampal Atrophy ◽  
Task Acquisition

Hippocampal atrophy is a widely used biomarker for Alzheimer's disease (AD), but the cost, time, and contraindications associated with magnetic resonance imaging (MRI) limit its use. Recent work has shown that a low-cost upper extremity motor task has potential in identifying AD risk. Fifty-four older adults (15 cognitively unimpaired, 24 amnestic Mild Cognitive Impairment, and 15 AD) completed six motor task trials and a structural MRI. Motor task acquisition significantly predicted bilateral hippocampal volume, controlling for age, sex, education, and memory. Thus, this motor task may be an affordable, non-invasive screen for AD risk and progression.

scholarly journals Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2645 ◽  
Author(s):  
Muazzam Maqsood ◽  
Faria Nazir ◽  
Umair Khan ◽  
Farhan Aadil ◽  
Habibullah Jamal ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transfer Learning ◽  
Brain Magnetic Resonance Imaging ◽  
Fine Tuning ◽  
Brain Cells ◽  
Daily Routine ◽  
Convolutional Network ◽  
Segmented Images ◽  
Multi Class Classification

Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.

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