QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning
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 fac...
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Elsevier
2025-06-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000330 |
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| author | Manish Bali Ved Prakash Mishra Anuradha Yenkikar Diptee Chikmurge |
| author_facet | Manish Bali Ved Prakash Mishra Anuradha Yenkikar Diptee Chikmurge |
| author_sort | Manish Bali |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dd2784b14799460193df29f2039f33ff |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-dd2784b14799460193df29f2039f33ff2025-02-04T04:10:26ZengElsevierMethodsX2215-01612025-06-0114103185QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learningManish Bali0Ved Prakash Mishra1Anuradha Yenkikar2Diptee Chikmurge3School of Engineering, Amity University Dubai Campus, Dubai, 25314, UAESchool of Engineering, Amity University Dubai Campus, Dubai, 25314, UAESchool of Engineering, Amity University Dubai Campus, Dubai, 25314, UAE; Department of CSE(AI), Vishwakarma Institute of Information Technology, Pune, 411048, Maharashtra, India; Corresponding author.School of Computer Engineering, MIT Academy of Engineering, Alandi (D), Pune, 412105, Maharashtra, IndiaDiabetic 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.http://www.sciencedirect.com/science/article/pii/S2215016125000330Hybrid Deep Learning-Quantum Transfer Learning for Diabetic Retinopathy Detection |
| spellingShingle | Manish Bali Ved Prakash Mishra Anuradha Yenkikar Diptee Chikmurge QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning MethodsX Hybrid Deep Learning-Quantum Transfer Learning for Diabetic Retinopathy Detection |
| title | QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning |
| title_full | QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning |
| title_fullStr | QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning |
| title_full_unstemmed | QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning |
| title_short | QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning |
| title_sort | quantumnet an enhanced diabetic retinopathy detection model using classical deep learning quantum transfer learning |
| topic | Hybrid Deep Learning-Quantum Transfer Learning for Diabetic Retinopathy Detection |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125000330 |
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