earticle

한국어

Early and accurate detection of Alzheimer’s Disease (AD) is critical for timely intervention. While prior deep learning models have achieved promising results using sagittal and coronal slices, the potential diagnostic contribution of axial views remains underexplored. In this study, we propose an enhanced dual-path attention-guided convolutional neural network (CNN) that integrates multi-view 2D T1-weighted MRI slices, including parasagittal, coronal, and axial planes, to improve classification of AD, mild cognitive impairment (MCI), and cognitively normal (CN) subjects. The architecture combines a localized SNeurodCNN branch with a global Inception-v4 backbone augmented by Convolutional Block Attention Module (CBAM). The addition of axial slices produced statistically significant improvements, increasing accuracy from 97.98% to 98.83% (p < 0.05) and enhancing AUC from 0.990 to 0.996. These results demonstrate that axial T1- weighted views provide unique diagnostic cues including ventricular enlargement and cortical thinning that are not fully captured by sagittal or coronal planes, thus offering complementary value in multi-view Alzheimer’s detection frameworks.