<p>Alzheimer’s disease (AD), is a
devastating neurodegenerative disorder that destroys the patient’s ability to
perform daily living task and eventually, takes their lives. Currently, there
are 5.8 million people in North America that suffer from AD. This number is
projected to by 13.8 million by the year of 2050. For many years, researchers
have been dedicated on performing automated diagnosis based on neuroimaging. There
are critical needs in two aspects of AD: 1) computer-based AD classification
with MRI images; 2) computer-based tools/system to enhance the AD patient’s
quality of life. We are addressing these two gaps via two specific objectives
in this study.</p>
<p>For objective 1, the task is to
develop a machine-learning based intelligent model for classification of AD
conditions (Normal Control [NC], Mild Cognitive Impairment [MCI], Alzheimer’s
disease [AD]) based on MRI images. Specifically, four different deep learning
models were developed and assessed. The overall average accuracy for AD
classification is 81.5%, provided by Multi-Layer-Output model.</p>
<p>For objective 2, a deep learning
model was developed and evaluated to recognitze three specific type of indoor
scenes (bedroom, living room and dining room). An accuracy of 97% was obtained.</p>
<p>This study showed the potential of
application in deep learning models for two different aspects of AD - disease
classification and intelligent model-based assistive device for AD patients.
Further research and development activities are recommended to further validate
these findings on larger and different datasets.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8980001 |
| Date | 16 August 2019 |
| Creators | Ke Xu (7023074) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/DEEP_LEARNING_MODELS_FOR_IMAGE-BASED_DISEASE_CLASSIFICATION_AND_ASSISTIVE_TECHNOLOGY_RELATED_TO_ALZHEIMER_S_DISEASE/8980001 |
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