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Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD), which is currently incurable. Early diagnosis of AD is essential for effective intervention since the World Alzheimer’s Report 2015 predicted the number of cases will triple by 2050. The 18F-FDG PET imaging technique, although effective in detecting metabolic activities in the brain, faces challenges such as low signal-to-noise ratios and limited data availability, which complicates the extraction of necessary lesion information that is effective for early stage MCI diagnose. To overcome these challenges, we introduce a novel deep learning-based model, ResGLPyramid, that combines convolution operations, MobileViTv3, and a global-local attention module (GLAM) block, to capture local and global representations. By utilizing a softened cross-entropy (SCE) objective function, the model reduces overfitting, improves generalization, and enhances the detection of subtle metabolic changes. The proposed model enhances the sensitivity and specificity of Alzheimer’s detection by leveraging local- and long-range interactions among critical diagnostic features that lead to more precise and efficient analyses. The experimental results show that the ResGLPyramid model achieved an accuracy of 92.75%, sensitivity of 90.80%, and specificity of 94.14% in classifying MCI and AD individuals. These results represent a 3.44% increase in accuracy and a 4.28% increase in specificity while the sensitivity is slightly lower compared to state-of-the-art methods. |
