Multimodal Machine Learning for Early Alzheimer's Disease Detection: Leveraging Cognitive Features, and Resnet-Based Image Analysis with SVM Tuning
Keywords:
Hyperopt, regularization, tuning, convolution block, Activation function, bottleneck, normalization, transition block, Tree-structured Parzen Estimators, hyperplane, CNNAbstract
With the growing prevalence of Alzheimer's disease globally, early and accurate diagnosis becomes imperative for effective intervention. Our study leverages a dataset comprising diverse biomarkers and cognitive features, employing advanced machine learning algorithms, particularly machine learning methods. The procedure starts with the extraction of features using ResNet, giving preference to skip connectors to minimize residual errors. ResNet50 is chosen for its exceptional capabilities in image analysis and classification. Parameters of the model are fine-tuned by adjusting them based on the discrepancy between expected and actual class scores. In the concluding layer, the SVM model undergoes tuning, specifically in the context of Alzheimer's detection for binary and multiclass assignments. Bayesian optimization with Hyperopt systematically explores the hyperparameter space, optimizing variables like kernel selection and regularization to enhance the model's effectiveness on the validation set. The proposed model demonstrates promising results in discriminating between Alzheimer's disease and normal cognitive aging, showcasing high sensitivity and specificity. The integration of multimodal data enhances the robustness of the model, providing a comprehensive and reliable tool for early detection. This research contributes to the ongoing efforts to develop precise and accessible diagnostic tools for Alzheimer's disease, with potential implications for timely clinical intervention and improved patient outcomes.
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