Diagnosis of Alzheimer's Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network.
Journal:
Journal of healthcare engineering
Published Date:
Jun 21, 2017
Abstract
. Error-free diagnosis of Alzheimer's disease (AD) from healthy control (HC) patients at an early stage of the disease is a major concern, because information about the condition's severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI) classification. However, distinguishing between Alzheimer's brain data and healthy brain data in older adults (age > 60) is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT) for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA). The reduced feature vector is sent to feed-forward neural network (FNN) to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.
Authors
Keywords
Aged
Aged, 80 and over
Algorithms
Alzheimer Disease
Brain
Case-Control Studies
Dementia
Diagnosis, Computer-Assisted
Female
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Middle Aged
Neural Networks, Computer
Pattern Recognition, Automated
Principal Component Analysis
Reproducibility of Results
Sensitivity and Specificity
Severity of Illness Index
Wavelet Analysis