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<p>The rapid convergence of quantum computing and deep learning is reshaping medical image analysis, enabling more accurate, interpretable, and computationally efficient diagnostic systems. This review, Analytical Study: Quantum Layered Inception ResNet-Based Optimization Techniques for Early Detection of Diabetic Retinopathy and Liver Disease, synthesizes insights from 100 peer-reviewed publications from 2017 to 2025 spanning Quantum Machine Learning (QML), Inception-ResNet architectures, and hybrid quantum-layered models in healthcare. The study identifies persistent challenges including dataset imbalance and heterogeneity, non-standardized preprocessing pipelines, limited cross-organ generalization, weak radiogenomic interpretability, and insufficient clinically explainable AI frameworks. To address these gaps, four key objectives are proposed: evaluating quantum-layered optimization within Inception-ResNet models, analysing trends in segmentation and classification performance, constructing a dataset–metric–hardware integration matrix, and defining a clinically interpretable quantum–deep learning framework. A central contribution of the review is its two-disease comparative paradigm, unifying ophthalmic fundus imaging and hepatic CT/MRI imaging within a single Quantum Layered Inception ResNet (QLIR) framework. The analysis demonstrates that QLIR effectively learns from 2D retinal images and 3D hepatic scans, outperforming classical architectures in generalization and training stability, supported by Quantum Natural Gradient (QNG) optimization. The study also introduces a Quantum Explainability Stack leveraging SHAP and LIME which achieves over 90% alignment with clinical interpretation, marking meaningful progress toward regulatory-compliant AI. An Integrated Analytical Data Mapping Table further organizes modality-specific preprocessing, segmentation, architectural tuning, and accuracy benchmarks across datasets such as EyePACS, Messidor, IDRiD, LiTS, and CHAOS. Using comparative meta-analysis and cross-modal benchmarking, the review shows that QLIR surpasses classical CNN and ResNet models, achieving 95 to 98% accuracy for diabetic retinopathy, 92–94% for liver disease, AUCs of 0.97 and 0.94, segmentation gains of 5 to 7%, and 8 to 10% improvements in prognostic modeling and radiomic feature learning. Hybrid GPU to QPU execution also converges 30 to 40% faster, confirming computational sustainability. Overall, the findings establish QLIR as a paradigm shift toward quantum-enabled precision healthcare which enhancing accuracy, interpretability, generalizability, and radiogenomic integration for clinically deployable AI systems.</p>

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Quantum Kernel Layered Inception ResNet-Based Optimization Techniques for Early Detection of Diabetic Retinopathy and Liver Disease

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Santosh Kumar Henge,
  • Deepak Mehta,
  • Sridhar Chintala,
  • Durgesh Nandan,
  • K. Suneetha,
  • Chanakya Kumar Hinge

摘要

The rapid convergence of quantum computing and deep learning is reshaping medical image analysis, enabling more accurate, interpretable, and computationally efficient diagnostic systems. This review, Analytical Study: Quantum Layered Inception ResNet-Based Optimization Techniques for Early Detection of Diabetic Retinopathy and Liver Disease, synthesizes insights from 100 peer-reviewed publications from 2017 to 2025 spanning Quantum Machine Learning (QML), Inception-ResNet architectures, and hybrid quantum-layered models in healthcare. The study identifies persistent challenges including dataset imbalance and heterogeneity, non-standardized preprocessing pipelines, limited cross-organ generalization, weak radiogenomic interpretability, and insufficient clinically explainable AI frameworks. To address these gaps, four key objectives are proposed: evaluating quantum-layered optimization within Inception-ResNet models, analysing trends in segmentation and classification performance, constructing a dataset–metric–hardware integration matrix, and defining a clinically interpretable quantum–deep learning framework. A central contribution of the review is its two-disease comparative paradigm, unifying ophthalmic fundus imaging and hepatic CT/MRI imaging within a single Quantum Layered Inception ResNet (QLIR) framework. The analysis demonstrates that QLIR effectively learns from 2D retinal images and 3D hepatic scans, outperforming classical architectures in generalization and training stability, supported by Quantum Natural Gradient (QNG) optimization. The study also introduces a Quantum Explainability Stack leveraging SHAP and LIME which achieves over 90% alignment with clinical interpretation, marking meaningful progress toward regulatory-compliant AI. An Integrated Analytical Data Mapping Table further organizes modality-specific preprocessing, segmentation, architectural tuning, and accuracy benchmarks across datasets such as EyePACS, Messidor, IDRiD, LiTS, and CHAOS. Using comparative meta-analysis and cross-modal benchmarking, the review shows that QLIR surpasses classical CNN and ResNet models, achieving 95 to 98% accuracy for diabetic retinopathy, 92–94% for liver disease, AUCs of 0.97 and 0.94, segmentation gains of 5 to 7%, and 8 to 10% improvements in prognostic modeling and radiomic feature learning. Hybrid GPU to QPU execution also converges 30 to 40% faster, confirming computational sustainability. Overall, the findings establish QLIR as a paradigm shift toward quantum-enabled precision healthcare which enhancing accuracy, interpretability, generalizability, and radiogenomic integration for clinically deployable AI systems.