Leveraging Quantum-Based Architectures for Robust Diagnostics

2025-11-15

AI Summary

This study addresses the precise classification of renal calculi, cysts, and tumors in CT images. We propose a quantum-classical hybrid deep learning framework that integrates a pretrained ResNet50 backbone with a quantum convolutional neural network (QCNN). Classical features are encoded into quantum states via angle encoding, and end-to-end training is performed on 8- and 12-qubit quantum simulators. Preprocessing includes non-local means denoising and CLAHE-based contrast enhancement; class imbalance is mitigated through data augmentation and weighted sampling. Experimental results demonstrate an overall accuracy of 99.0%. Under the 12-qubit configuration, cyst recall reaches 100% and tumor F1-score attains 99.56%. Confusion matrices reveal minimal misclassification, confirming substantial improvements in robustness for rare lesion detection and enhanced clinical interpretability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The objective of this study is to diagnose and differentiate kidney stones, cysts, and tumors using Computed Tomography (CT) images of the kidney. This study leverages a hybrid quantum-classical framework in this regard. We combine a pretrained ResNet50 encoder, with a Quantum Convolutional Neural Network (QCNN) to explore quantum-assisted diagnosis. We pre-process the kidney images using denoising and contrast limited adaptive histogram equalization to enhance feature extraction. We address class imbalance through data augmentation and weighted sampling. Latent features extracted by the encoder are transformed into qubits via angle encoding and processed by a QCNN. The model is evaluated on both 8-qubit and 12-qubit configurations. Both architectures achieved rapid convergence with stable learning curves and high consistency between training and validation performance. The models reached a test accuracy of 0.99, with the 12-qubit configuration providing improvements in overall recall and precision, particularly for Cyst and Tumor detection, where it achieved perfect recall for Cysts and a tumor F1-score of 0.9956. Confusion matrix analysis further confirmed reliable classification behavior across all classes, with very few misclassifications. Results demonstrate that integrating classical pre-processing and deep feature extraction with quantum circuits enhances medical diagnostic performance.

Problem

• Diagnose kidney stones cysts tumors using CT images
• Apply hybrid quantum-classical framework for medical diagnosis
• Address class imbalance through data augmentation techniques

Innovation

• Hybrid quantum-classical framework for kidney diagnosis
• Quantum CNN processes encoder features via angle encoding
• Classical preprocessing and data augmentation enhance quantum performance