Probabilistic principal component analysis and long short-term memory classifier for automatic detection of Alzheimer’s disease using MRI brain images

Abstract

The automatic recognition and classification of Alzheimer disease utilizing magnetic resonance imaging is a hard task, due to the complexity and variability of the size, location, texture and shape of the lesions. The objective of this study is to propose a proper feature dimensional reduction and classification approach to improve the performance of Alzheimer disease recognition and classification. At first, the input brain images were acquired from Open Access Series of Imaging Studies (OASIS) and National Institute of Mental Health and Neuro Sciences (NIMHANS) databases. Then, the image pre-processing and feature extraction were attained by applying Contrast Limited Adaptive Histogram Equalization (CLAHE) and Discrete Wavelet Transform (DWT) approach to denoise and extract the feature vectors from the images. In addition, Probabilistic Principal Component Analysis (PPCA) was used to diminish the extracted features dimension that effectively lessen the “curse of dimensionality” concern. At last, Long Short-Term Memory (LSTM) classifier was employed to classify the brain images as Alzheimer disease, normal, and Mild Cognitive Impairment (MCI). From the simulation outcome, the proposed system attained better performance compared to the existing systems and showed 3–11% improvement in recognition accuracy.

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

“…WT can detect features overlooked by other feature extraction methods, such as breakdown points and discontinuities. Several other studies have also utilized WT as a tool for feature extraction in the form of wavelet coefficients from MRI images [26], [39], [40], [41]. However, WT's major limitation is its inability to identify curved edges, which in some cases causes false alarms.…”