CurvMRI: A Curvelet Transform-Based MRI Approach for Alzheimer's Disease Detection

Abstract

Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, as projected in the related Magnetic Resonance Imaging (MRI). The early identification of AD is essential for preventive treatment; thus, different machine/deep learning (ML/DL) approaches applied on MRI scans from patients at different AD stages have been proposed in recent years. Here, a new method, namely CurvMRI, for AD detection from MRI images using Fast Curvelet Transform (FCT) is proposed. The approach is realized via a sequence of steps, i.e., feature extraction, feature reduction, and classification. MRI images are obtained from a Kaggle dataset containing five AD stages, from where Cognitive Normal (CN) (493/87 (training/testing)) and AD (145/26) MRI images were selected for binary classification. The FCT with wrapping method was implemented, and higher-order statistics, such as kurtosis and skewness, as well as energy and variance, were then used to extract features from the curvelet sub-bands. Features were then concatenated and fed to a Support Vector Machine (SVM) classifier, giving an accuracy of 77.6%, which outperforms the most common DL classification approaches applied to the same dataset. These results showcase the potentiality of the proposed CurvMRI to efficiently discriminate AD from CN in MRI images, and provide a fast and easy to implement ML tool for assisting physicians in AD detection.