Alzheimer's Disease Classification Using Retinal OCT: TransnetOCT and Swin Transformer Models
This work addresses early detection of Alzheimer's disease for clinical diagnostics, but it is incremental as it applies existing deep learning methods to a specific medical imaging task.
The paper tackled Alzheimer's disease classification using retinal OCT images, achieving an average accuracy of 98.18% with TransNetOCT and 93.54% with Swin Transformer models.
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.