Abstract
Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes lesions on the retina that affect vision. Late detection of DR can lead to irreversible blindness. The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly. While, Classical Transfer learning models are extensively used for computer aided detection of DR; however, their maintenance costs limits detection performance rate. Therefore, Quantum Transfer learning is a better option to address this problem in an optimized manner. The significance of Hybrid quantum transfer learning approach includes that it performs heuristically. Thus, our proposed methodology aims to detect DR using a hybrid quantum transfer learning approach. To build our model we extract the APTOS 2019 Blindness Detection dataset from Kaggle and used inception-V3 pre-trained classical neural network for feature extraction and Variational Quantum classifier for stratification and trained our model on Penny Lane default device, IBM Qiskit BasicAer device and Google Cirq Simulator device. Both models are built based on PyTorch machine learning library. We bring about a contrast performance rate between classical and quantum models. Our proposed model achieves an accuracy of 93%–96% on the quantum hybrid model and 85% accuracy rate on the classical model. So, quantum computing can harness quantum machine learning to do work with power and efficiency that is not possible for classical computers.
Highlights
Diabetic retinopathy (DR) is the most common form of diabetic eye disease [1]
It is quite evident from the majority of the work in diabetic retinopathy detection revolves around the use of various transfer learning models and performance comparison of these models
First matrix is based on Classical Transfer learning model and other three matrices are Quantum Transfer learning models which are Trained on Google Cirq Simulator, IBM Qiskit BasicAer device and penny lane default device) [14]
Summary
Diabetic retinopathy (DR) is the most common form of diabetic eye disease [1]. Diabetic retinopathy usually only affects people who have chronic diabetes (diagnosed or undiagnosed). DR occurs due to the damage of tiny blood vessels in the retina due to chronic diabetics This may cause hemorrhages, exudates and even swelling of the retina can cause blind spots blurry vision. A breakthrough in the field of quantum computing can help in giving the ophthalmologist a second opinion to solve this problem by using hybrid quantum transfer learning approach. This quantum approach can result into more efficient detection of DR in patients as compared to the classical transfer learning [3,4]. This work presents a hybrid approach of quantum learning model for DR detection. Quantum computing approaches are great for solving optimization problems as compared to classical computing approaches
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