Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD. Recent studies have employed machine learning to detect and classify AD. Deep learning models have also been increasingly utilized with varying degrees of success. This paper presents a novel hybrid approach for early detection and classification of AD using structural MRI (sMRI). The proposed model employs a unique combination of machine learning and deep learning approaches to optimize the precision and accuracy of the detection and classification of AD. The proposed approach surpassed multi-modal machine learning algorithms in accuracy, precision, and F-measure performance measures. Results confirm an outperformance compared to the state-of-the-art in AD versus CN and sMCI versus pMCI paradigms. Within the CN versus AD paradigm, the designed model achieves 91.84% accuracy on test data.
Keywords: Alzheimer’s disease; Classification; Convolutional neural network; Hybrid features learning; Machine learning.
© 2024. The Author(s).