Abstract
Diabetic Retinopathy (DR), a prevalent complication in diabetes patients, can lead to vision impairment due to lesions formed on the retina. Detecting DR at an advanced stage often results in irreversible blindness. The traditional process of diagnosing DR through retina fundus images by ophthalmologists is not only time-intensive but also expensive. While classical transfer learning models have been widely adopted for computer-aided detection of DR, their high maintenance costs can hinder their detection efficiency. In contrast, Quantum Transfer Learning offers a more effective solution to this challenge. This approach is notably advantageous because it operates on heuristic principles, making it highly optimized for the task. Our proposed methodology leverages this hybrid quantum transfer learning technique to detect DR. To construct our model, we utilize the APTOS 2019 Blindness Detection dataset, available on Kaggle. We employ the ResNet-18, ResNet34, ResNet50, ResNet101, ResNet152 and Inception V3, pre-trained classical neural networks, for the initial feature extraction. For the classification stage, we use a Variational Quantum Classifier. Our hybrid quantum model has shown remarkable results, achieving an accuracy of 97% for ResNet-18. This demonstrates that quantum computing, when integrated with quantum machine learning, can perform tasks with a level of power and efficiency unattainable by classical computers alone. By harnessing these advanced technologies, we can significantly improve the detection and diagnosis of Diabetic Retinopathy, potentially saving many from the risk of blindness. Keywords: Diabetic Retinopathy, Quantum Transfer Learning, Deep Learning
Abstract (translated)
糖尿病视网膜病变（DR），是糖尿病患者常见的并发症，可能导致视网膜形成病变，从而导致视力减退。在DR的晚期阶段，常常会导致不可逆的失明。通过眼科医生对视网膜 fundus 图像的诊断方式来传统地诊断DR，不仅费时，而且费用高昂。虽然经典的迁移学习模型已广泛应用于DR的计算机辅助检测，但它们的高维护成本可能会阻碍其检测效率。相比之下，量子迁移学习为解决这一挑战提供了更有效的解决方案。由于这种方法基于启发式原则，因此它在任务上具有高度优化能力。在我们的研究中，我们利用基于APTOS 2019盲人检测数据集的混合量子迁移学习方法来检测DR。为了构建我们的模型，我们利用了Kaggle上可用的APTOS 2019盲人检测数据集。我们使用预训练的ResNet-18、ResNet34、ResNet50、ResNet101、ResNet152和Inception V3古典神经网络进行初始特征提取。在分类阶段，我们使用变分量子分类器。我们的混合量子模型已经取得了显著的成果，ResNet-18的准确率达到了97%。这表明，结合量子计算与量子机器学习，可以实现与经典计算机无法比拟的权力和效率。通过利用这些先进技术，我们可以显著提高糖尿病视网膜病变的检测和诊断，从而可能拯救许多患者免于失明之险。关键词：糖尿病视网膜病变，量子迁移学习，深度学习
URL
https://arxiv.org/abs/2405.01734
