IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 335 10.51583/IJLTEMAS.2026.150800141 Machine Learning A Comparative Study of CNN and Vision Transformer Models in Alzheimer's Detection Stephen D Research Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India India R Samuel Rajesh Babu Dr. Associate Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India India 15 8 1952 1960 03 09 2026 08 09 2026 26 09 2026 Index Terms Alzheimer's disease MRI classification hierarchical attention vision transformers deep learning HierAttNet convolutional neural networks. Abstract

In this work, a novel deep learning framework the Hierarchical Attention Network (HierAttNet) is proposed to replace the previously used Vision Transformer (ViT-B/16) and all CNN baselines in the literature to stage Alzheimer's disease (AD) from MRI brain images. The ConvNeXt-Small feature extractor and the two-scale cross-attention module alternately attend to the local patch level and the region level in a hierarchical fashion, which is not reported in the AD neuroimaging literature. We compare HierAttNet with six additional state-of-the-art algorithms, which were not presented in the original paper: EfficientNetV2-S, Swin-T, MobileViT-S, CrossViT-15, DeiT-S and ConvNeXt-S. The following experiments are performed on the Kaggle Alzheimer's MRI dataset (6,400+ images, 4 stages of Alzheimer's). HierAttNet obtains 99.91% accuracy and F1=0.9993 outperforming all the evaluated models and even the previous state-of-the-art ViT-B/16 (99.87%). The performance of all models consistently dropped with data augmentation. In this work, we introduce HierAttNet as a new state-of-the-art in Alzheimer's MRI classification, and a stringent benchmark for subsequent multimodal diagnostics studies.

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