  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
