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<p>Prevention of diabetic retinopathy (DR), the major cause of visual impairment and blindness, requires early diagnosis and risk assessment. Traditional DR detection machine learning methods misclassify early and moderate DR phases due to inadequate generalizability, overfitting to individual datasets, and insufficient multimodal feature use. These papers provide a Quantum-Boosted Ensemble Learning Framework (QBEL-DR) that uses deep learning, ensemble classifiers, and quantum-enhanced feature optimization to improve DR risk factor assessment and staging prediction. In the QBEL-DR model, a Multi-Scale Transformer Convolutional Neural Network (MT-CNN) extracts fundus microvascular characteristics from color fundus photography (CFP), fluorescein angiography (FA), and optical coherence tomography. By removing redundant data, Quantum Feature Selection (QFS) optimizes a Hybrid Ensemble Classifier (HEC-Boost) that combines XGBoost, Random Forest, and LightGBM to identify vascular abnormalities. A Graph Attention Networks (GAT)-based Temporal development Prediction Module (TPPM) examines disease development from early NPDR to PDR.</p>

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Quantum-boosted ensemble learning framework for early risk stratification and staging prediction of diabetic retinopathy using multimodal fundus imaging and deep feature fusion

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • B. A. Nithya,
  • Sameeruddin Khan

摘要

Prevention of diabetic retinopathy (DR), the major cause of visual impairment and blindness, requires early diagnosis and risk assessment. Traditional DR detection machine learning methods misclassify early and moderate DR phases due to inadequate generalizability, overfitting to individual datasets, and insufficient multimodal feature use. These papers provide a Quantum-Boosted Ensemble Learning Framework (QBEL-DR) that uses deep learning, ensemble classifiers, and quantum-enhanced feature optimization to improve DR risk factor assessment and staging prediction. In the QBEL-DR model, a Multi-Scale Transformer Convolutional Neural Network (MT-CNN) extracts fundus microvascular characteristics from color fundus photography (CFP), fluorescein angiography (FA), and optical coherence tomography. By removing redundant data, Quantum Feature Selection (QFS) optimizes a Hybrid Ensemble Classifier (HEC-Boost) that combines XGBoost, Random Forest, and LightGBM to identify vascular abnormalities. A Graph Attention Networks (GAT)-based Temporal development Prediction Module (TPPM) examines disease development from early NPDR to PDR.