Generative Adversarial Networks Based on Fine-Grained Image Recognition for the Progression Prediction of Progressive Mild Cognitive Impairment

Abstract

Progressive mild cognitive impairment (pMCI) often develops into Alzheimer's disease (AD), whereas stable mild cognitive impairment (sMCI) remains cognitively unchanged. Therefore, early identification of pMCI based on multimodal neuroimaging data (e.g., MRI, PET) is clinically valuable. However, limited multimodal data reduces complementary information across modalities and degrades prediction performance. Existing generative adversarial networks (GANs) often overlook local information when synthesizing cross-modal neuroimages, leading to suboptimal image quality. Motivated by these shortcomings, we propose a generative adversarial network (FGGAN) based on fine-grained image recognition for cross-modal image synthesis and pMCI progression prediction. FGGAN comprises a GAN, a feature depth extraction (FDE) module, and a classifier module. The GAN synthesizes high-quality missing modality data by leveraging local and global cues from the input image, while extracting multimodal feature representations. The FDE refines semantic features to improve feature adaptation for the classifier, which predicts pMCI progression from fused multimodal features. Results from the ADNI dataset indicate that FGGAN achieves superior performance in image synthesis quality and disease classification.