Accurate diagnosis of Alzheimer’s Disease (AD) remains a critical challenge, despite its growing prevalence. Recent advancements in neuroimaging have opened new avenues for enhancing diagnostic precision through deep learning techniques. In this study, we introduce a new method that combines diverse pretrained CNN models and attention mechanisms. We select the most suitable model for each view (axial, coronal and sagittal) after a series of experiments. This method aims to capture a broader range of features from the multi-view input sMRI images, leading to improved classification performance. The method presented here was tested on ADNI dataset. The obtained results prove the efficiency of our proposed method ( https://adni.loni.usc.edu/ ).