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.

翻译：本研究旨在利用肾脏计算机断层扫描（CT）图像对肾结石、囊肿和肿瘤进行诊断与鉴别。为此，我们采用了一种混合量子-经典计算框架。我们结合了预训练的ResNet50编码器与量子卷积神经网络（QCNN），以探索量子辅助诊断的潜力。通过去噪和对比度受限自适应直方图均衡化对肾脏图像进行预处理，以增强特征提取能力。针对类别不平衡问题，我们采用了数据增强和加权采样策略。编码器提取的潜在特征通过角度编码转换为量子比特，并由QCNN进行处理。模型在8量子比特和12量子比特两种配置下进行了评估。两种架构均实现了快速收敛，学习曲线稳定，且训练与验证性能高度一致。模型测试准确率达到0.99，其中12量子比特配置在整体召回率和精确度方面表现更优，尤其在囊肿和肿瘤检测中：囊肿召回率达到完美值，肿瘤F1分数为0.9956。混淆矩阵分析进一步证实了模型在所有类别中均表现出可靠的分类性能，误分类极少。结果表明，将经典预处理与深度特征提取同量子电路相结合，能够有效提升医学诊断性能。