ConvNeXt-Driven Detection of Alzheimer's Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes.

ABSTRACT: Background: Alzheimer's disease (AD) is a leading worldwide cause of cognitive impairment, necessitating accurate, inexpensive diagnostic tools to enable early recognition. Methods: In this study, we present a robust deep learning approach for AD classification based on structural MRI scans, ConvNeXt, an emergent convolutional architecture inspired by vision transformers. We introduce AlzaSet, a clinically curated T1-weighted MRI dataset of 79 subjects (63 with Alzheimer's disease [AD], 16 cognitively normal controls [NC]) acquired on a 1.5 T Siemens Aera in axial, coronal, and sagittal planes, respectively (12,947 slices in total). Images are neuroradiologist-labeled. Results are reported per plane, with awareness of the class imbalance at the subject level. We further prese

SUBMITTER: Basereh M 

PROVIDER: S-EPMC12691013 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature