用视网膜OCT图像识别阿尔茨海默病，准确率超98%。
Alzheimer's Disease Classification Using Retinal OCT: TransnetOCT and Swin Transformer Models
- 基于TransNetOCT和Swin Transformer模型分析视网膜OCT图像
- 分割后图像分类准确率达98.91%，整体准确率98.18%
- 适合临床早期神经退行性疾病筛查使用
视网膜光学相干断层扫描（OCT）图像作为神经退行性疾病的重要生物标志物，其在阿尔茨海默病（AD）早期检测中的应用面临挑战。本文利用深度学习技术对AD患者与健康对照组（CO）的OCT图像进行分类，以提升诊断能力。原始OCT图像经ImageJ预处理后输入多种深度学习模型进行评估。结果显示，TransNetOCT模型在五折交叉验证下对原始图像的平均准确率为98.18%，对分割后图像达98.91%；Swin Transformer模型准确率为93.54%。实验表明，TransNetOCT与Swin Transformer具备可靠区分AD与CO的能力，有望推动临床诊断流程优化。
原文摘要 · Abstract (English)
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.
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