Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models.

ABSTRACT: Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we developed Miniset-DenseSENet, a convolutional neural network combining DenseNet121 with a Squeeze-and-Excitation attention mechanism, using 190 autopsy brain images from the Gregory Laboratory at the University of Aberdeen. The model distinguishes controls, ALS patients with no cognitive impairment, and ALS patients with cognitive impairment (ALS-frontotemporal dementia) with 97.37% accuracy, addressing a significant challenge in overlapping neurodegenerative disorders involving TDP-43 proteinopathy. Miniset-DenseSENet outperformed other trans

SUBMITTER: Xia Y 

PROVIDER: S-EPMC11868594 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature