Alzheimer’s Disease Detection from Fused PET and MRI Modalities Using an Ensemble Classifier

• Published in: Machine learning and knowledge extraction Vol. 5; no. 2; pp. 512 - 538
• Main Authors: Shukla, Amar, Tiwari, Rajeev, Tiwari, Shamik
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Algorithms; Alzheimer's disease; Artificial intelligence; Automatic classification; Biomarkers; Classification; Classifiers; Cognition & reasoning; Cognitive ability; Diagnosis; ensemble learning; feature-level fusion; Glucose; image fusion; Machine learning; Magnetic resonance imaging; Medical imaging; Metabolism; Methods; multimodality; Neuroimaging; Neurological disorders; PET imaging; Positron emission; India
• ISSN: 2504-4990; 2504-4990
• DOI: 10.3390/make5020031

Alzheimer’s disease (AD) is an old-age disease that comes in different stages and directly affects the different regions of the brain. The research into the detection of AD and its stages has new advancements in terms of single-modality and multimodality approaches. However, sustainable techniques for the detection of AD and its stages still require a greater extent of research. In this study, a multimodal image-fusion method is initially proposed for the fusion of two different modalities, i.e., PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging). Further, the features obtained from fused and non-fused biomarkers are passed to the ensemble classifier with a Random Forest-based feature selection strategy. Three classes of Alzheimer’s disease are used in this work, namely AD, MCI (Mild Cognitive Impairment) and CN (Cognitive Normal). In the resulting analysis, the Binary classifications, i.e., AD vs. CN and MCI vs. CN, attained an accuracy (Acc) of 99% in both cases. The class AD vs. MCI detection achieved an adequate accuracy (Acc) of 91%. Furthermore, the Multi Class classification, i.e., AD vs. MCI vs. CN, achieved 96% (Acc).