Deep Learning Models for Image-Based Disease Classification and Assistive Technology Related to Alzheimer’s Disease

Ke Xu, Purdue University

Abstract

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. 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. 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. 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.

Degree

Ph.D.

Advisors

Panigrahi, Purdue University.

Subject Area

Medical imaging|Pathology|Aging|Artificial intelligence|Computer science|Neurosciences

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