对比UNet与堆叠UNet在糖尿病视网膜病变检测中的表现
Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures
- 采用UNet和堆叠UNet模型分析眼底图像
- 堆叠UNet达到93.32%准确率，优于UNet的92.81%
- 适合眼科影像自动诊断研究者参考
糖尿病视网膜病变（Diabetic Retinopathy, DR）是糖尿病的严重并发症，可导致视力丧失。大规模糖尿病患者筛查需求推动了全自动计算机辅助诊断系统的发展。在深度学习框架中，卷积神经网络（CNN）在分析眼底图像检测DR方面展现出巨大潜力。然而，该领域仍面临挑战：高质量标注数据稀缺，图像质量差异大，类别不平衡等问题影响模型可靠性。本文基于亚太远程眼科协会数据集（APTOS Asia Pacific Tele-Ophthalmology Society Dataset），评估了两种基于CNN的模型——UNet与堆叠UNet。模型将图像分为五类，从0（无病变）到4（增殖性病变）。实验结果显示，UNet的准确率为92.81%，堆叠UNet提升至93.32%。
原文摘要 · Abstract (English)
Diabetic Retinopathy DR is a severe complication of diabetes. Damaged or abnormal blood vessels can cause loss of vision. The need for massive screening of a large population of diabetic patients has generated an interest in a computer-aided fully automatic diagnosis of DR. In the realm of Deep learning frameworks, particularly convolutional neural networks CNNs, have shown great interest and promise in detecting DR by analyzing retinal images. However, several challenges have been faced in the application of deep learning in this domain. High-quality, annotated datasets are scarce, and the variations in image quality and class imbalances pose significant hurdles in developing a dependable model. In this paper, we demonstrate the proficiency of two Convolutional Neural Networks CNNs based models, UNET and Stacked UNET utilizing the APTOS Asia Pacific Tele-Ophthalmology Society Dataset. This system achieves an accuracy of 92.81% for the UNET and 93.32% for the stacked UNET architecture. The architecture classifies the images into five categories ranging from 0 to 4, where 0 is no DR and 4 is proliferative DR.
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