Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI - Lim, Bing Yan; Lai, Khin Wee; Haiskin, Khairunnisa; Kulathilake, K. A. Saneera Hemantha; Ong, Zhi Chao; Hum, Yan Chai; Dhanalakshmi, Samiappan; Wu, Xiang; Zuo, Xiaowei (Frontiers in Aging Neuroscience, 2022)
Alzheimer’s disease (AD), an irreversible neurodegenerative disorder that inflicts the majority cases of dementia, wherein patients suffer progressive memory loss and cognitive function decline. Despite having no drugs for curing, early detection of AD allows the provision of preventive treatment to control the disease progression. The objective of this study is to develop a computer-aided system based on Deep Learning model to identify AD from cognitively normal and its early stage, mild cognitive impairment (MCI), using only structural MRI (sMRI). To achieve this objective, we have proposed a multi-class classification using 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from the 3D MRI and being fed as input to the Convolutional Neural Network (CNN) to perform multiclass classification. Three different models were being experimented namely a CNN from scratch, VGG-16, and ResNet-50. The convolutional base of VGG-16 and ResNet-50 trained on ImageNet dataset were used as a feature extractor. Additionally, a new densely connected classifier was added on top of the convolutional base to perform classification.