A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices

• Published in: Computational and mathematical methods in medicine 2022-08, Vol.2022, p.8680737-11
• Main Authors: Sethi, Monika, Rani, Shalli, Singh, Aman, Mazón, Juan Luis Vidal
• Format: Article
• Language: English
• Subjects: Alzheimer Disease - diagnostic imaging; Brain - diagnostic imaging; Cognitive Dysfunction - diagnostic imaging; Humans; Magnetic Resonance Imaging - methods; Neural Networks, Computer; Neuroimaging - methods
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2022/8680737

Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer’s disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN’s receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN’s capabilities in feature extraction and SVM in classification. The objective of this research is to build a hybrid CNN-SVM model for classifying AD using the MRI ADNI dataset. Experimental results reveal that the hybrid CNN-SVM model outperforms the CNN model alone, with relative improvements of 3.4%, 1.09%, 0.85%, and 2.82% on the testing dataset for AD vs. cognitive normal (CN), CN vs. mild cognitive impairment (MCI), AD vs. MCI, and CN vs. MCI vs. AD, respectively. Finally, the proposed approach has been further experimented on OASIS dataset leading to accuracy of 86.2%.