QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning
Abstract
Diabetic Retinopathy (DR), a diabetes-related eye condition, damages retinal blood vessels and can lead to vision loss if undetected early. Precise diagnosis is challenging due to subtle, varied symptoms. While classical deep learning (DL) models like CNNs and ResNet's are widely used, they face resource and accuracy limitations. Quantum computing, leveraging quantum mechanics, offers revolutionary potential for faster problem-solving across fields like cryptography, optimization, and medicine. This research introduces QuantumNet, a hybrid model combining classical DL and quantum transfer learning to enhance DR detection. QuantumNet demonstrates high accuracy and resource efficiency, providing a transformative solution for DR detection and broader medical imaging applications. The method is as follows:• Evaluate three classical deep learning models—CNN, ResNet50, and MobileNetV2—using the APTOS 2019 blindness detection dataset on Kaggle to identify the best-performing model for integration.• QuantumNet combines the best-performing classical DL model for feature extraction with a variational quantum classifier, leveraging quantum transfer learning for enhanced diagnostics, validated statistically and on Google Cirq using standard metrics.• QuantumNet achieves 94.11 % accuracy, surpassing classical DL models and prior research by 11.93 percentage points, demonstrating its potential for accurate, efficient DR detection and broader medical imaging applications.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.