March 3, 2026Open Access

Feature Integration of 18 FFDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection

Key Points

Sensitivity to cognitive decline significantly increased with fusion-based approaches, enhancing detection capabilities.
Combining PET imaging with deep learning shows a marked improvement in diagnostic performance metrics.
Observational analysis employed multimodal strategies for quantifying features, leading to improved accuracy in classifying cognitive decline.
Such advancements may support broader applications of PET imaging in clinical environments, enhancing patient assessment.

Abstract

Combining PET imaging with region-level quantification and deep learning improves diagnostic performance over single-feature based models. Notably, fusion-based approaches enhanced sensitivity to cognitive decline. This multimodal strategy offers a more data-efficient and accurate approach for classifying cognitive decline and supports broader PET application in clinical settings.

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Authors

Yeji Lee

Georgia Institute of Technology

S. H. Kim

Pusan National University

Sangil Kim

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Institutions

Pusan National University

Howard University

Defiance College

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Feature Integration of 18 FFDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection

Key Points

Abstract

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Discussion

Authors

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References and Citations

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