Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer's Disease Using Structural MRI

Bing Yan Lim; Khin Wee Lai; Khairunnisa Haiskin; K A Saneera Hemantha Kulathilake; Zhi Chao Ong; Yan Chai Hum; Samiappan Dhanalakshmi; Xiang Wu; Xiaowei Zuo

doi:10.3389/fnagi.2022.876202

Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer's Disease Using Structural MRI

Front Aging Neurosci. 2022 Jun 2:14:876202. doi: 10.3389/fnagi.2022.876202. eCollection 2022.

Authors

Bing Yan Lim¹, Khin Wee Lai¹, Khairunnisa Haiskin¹, K A Saneera Hemantha Kulathilake², Zhi Chao Ong³, Yan Chai Hum⁴, Samiappan Dhanalakshmi⁵, Xiang Wu⁶, Xiaowei Zuo⁷

Affiliations

¹ Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
² Department of Computing, Faculty of Applied Sciences, Rajarata University of Sri Lanka, Anuradhapura, Sri Lanka.
³ Department of Mechanical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
⁴ Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Petaling Jaya, Malaysia.
⁵ Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India.
⁶ School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou, China.
⁷ Department of Psychiatry, The Affiliated Xuzhou Oriental Hospital of Xuzhou Medical University, Xuzhou, China.

Abstract

Alzheimer's disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite the lack of a cure, early detection of Alzheimer's disease permits the provision of preventive medication to slow the disease's progression. The objective of this project is to develop a computer-aided method based on a deep learning model to distinguish Alzheimer's disease (AD) from cognitively normal and its early stage, mild cognitive impairment (MCI), by just using structural MRI (sMRI). To attain this purpose, we proposed a multiclass classification method based on 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from 3D MRI and fed into the convolutional neural network (CNN) for multiclass classification. Three separate models were tested: a CNN built from scratch, VGG-16, and ResNet-50. As a feature extractor, the VGG-16 and ResNet-50 convolutional bases trained on the ImageNet dataset were employed. To achieve classification, a new densely connected classifier was implemented on top of the convolutional bases.

Keywords: Alzheimer’s disease; deep learning; magnetic resonance imaging; mild cognitive impairment; prediction.