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.

翻译：视网膜光学相干断层扫描（OCT）图像是神经退行性疾病的生物标志物，此类疾病的患病率正不断上升。利用视网膜OCT实现阿尔茨海默病的早期检测是一项具有挑战性的关键任务。本研究采用先进的深度学习技术，对阿尔茨海默病（AD）患者与健康对照（CO）受试者的视网膜OCT图像进行分类，旨在通过高效的图像分析提升诊断能力。在所提出的模型中，原始OCT图像经ImageJ预处理后，输入多种深度学习模型以评估其准确性。结果表明，在五折交叉验证中，最佳分类架构TransNetOCT对输入OCT图像的平均准确率达到98.18%，对分割后OCT图像的平均准确率为98.91%，优于其他模型；而Swin Transformer模型的准确率为93.54%。评估准确度指标证明了TransNetOCT与Swin Transformer模型能够可靠地区分AD与CO受试者，这有助于提升临床诊断流程的潜力。