Abstract
BACKGROUND: Alzheimer's disease (AD) is a progressive and irreversible brain disorder, characterized by a gradual decline in cognitive and memory function, with memory loss being one of the most prominent symptoms. Accurate and early diagnosis of AD is essential for effective management and treatment. Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) are widely utilized neuroimaging modalities for diagnosing AD due to their ability to provide complementary structural and functional insights into brain abnormalities. METHODS: This study introduces a novel computer-aided diagnosis system that integrates sMRI and PET data using Fuzzy Cognitive Maps (FCM) to improve diagnostic accuracy. The research is conducted using the ADNI dataset, where preprocessing of sMRI and PET images is performed using FSL and statistical parametric mapping tools, respectively. In a key innovation, features extracted from both modalities are fused and dimensionality reduction is achieved through an Autoencoder model. The reduced feature set is then classified using FCM, Support Vector Machine, k-Nearest Neighbors, and Multilayer Perceptron. RESULTS: The FCM-based approach demonstrates superior performance, achieving the highest accuracy of 93.71%, surpassing other classifiers tested. CONCLUSIONS: This study underscores the effectiveness of integrating FCM with multimodal neuroimaging data and highlights its potential for enhancing the early and reliable diagnosis of AD.