Alzheimer's Disease Classification Using Retinal OCT: TransnetOCT and Swin Transformer Models
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
Retinal optical coherence tomography (OCT) images are the biomarkers for
neurodegenerative diseases, which are rising in prevalence. Early detection of
Alzheimer's disease using retinal OCT is a primary challenging task. This work
utilizes advanced deep learning techniques to classify retinal OCT images of
subjects with Alzheimer's disease (AD) and healthy controls (CO). The goal is
to enhance diagnostic capabilities through efficient image analysis. In the
proposed model, Raw OCT images have been preprocessed with ImageJ and given to
various deep-learning models to evaluate the accuracy. The best classification
architecture is TransNetOCT, which has an average accuracy of 98.18% for input
OCT images and 98.91% for segmented OCT images for five-fold cross-validation
compared to other models, and the Swin Transformer model has achieved an
accuracy of 93.54%. The evaluation accuracy metric demonstrated TransNetOCT and
Swin transformer models capability to classify AD and CO subjects reliably,
contributing to the potential for improved diagnostic processes in clinical
settings.
