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Neurocomputing

Hybrid classical–quantum neural networks enhanced by quantum architecture search for coronary artery stenosis detection

Shao‐Chun Li, Junzhi Cui, Jingli Ren

Published Dec 7, 2024• 16 citations
Publisher
DOI
10.1016/j.neucom.2024.129111

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Coronary artery disease is the leading cause of mortality rate worldwide. This condition is caused by an abnormal narrowing or occlusion of the artery lumen, known as atherosclerosis (a specific type of stenosis). Currently, many imaging techniques have been tested; nevertheless, conventional X-ray Coronary Angiography (XCA) remains the gold standard for coronary artery disease diagnosis, such as atherosclerosis. This study presents a Hybrid Quantum-Convolutional Neural Network to detect coronary artery atherosclerosis in XCA images automatically. It employs a Quantum Convolutional Layer (QCL) that resembles a typical Convolutional Layer. However, unlike the convolution operation where a classical convolutional filter is applied, a QCL transforms the input using a quantum circuit. This QCL generates a multichannel image from a single channel XCA that feeds a traditional Convolutional Neural Network (CNN). The CNN was trained and optimized using two different architectures previously employed for atherosclerosis detection, viz. a DenseNet-based and a VGG-based architectures. Additionally, a comparison of Stochastic Gradient Descent (SGD) and SGD with Momentum (SGDM) were conducted. These architectures were analyzed and compared using a real dataset composed of 250 real XCA images (split into 125 images for testing and 125 for training) regarding their training without the QCL preprocessing and SGDM. The results showed that the CNNs trained with the outcome of the QCL surpasses the baseline CNNs performance. Herein, the best atherosclerosis detection reached an accuracy of 83.36%, while in other metrics such as precision, sensitivity, specificity, and F1-score, achieved an 81.52%, 86.67%, 83.99%, and 80.00%, respectively.

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IEEE Access•Jan 1, 2022•44 citations
Coronary artery disease is the leading cause of mortality rate worldwide. This condition is caused by an abnormal narrowing or occlusion of the artery lumen, known as atherosclerosis (a specific type of stenosis). Currently, many imaging techniques have been tested; nevertheless, conventional X-ray Coronary Angiography (XCA) remains the gold standard for coronary artery disease diagnosis, such as atherosclerosis. This study presents a Hybrid Quantum-Convolutional Neural Network to detect coronary artery atherosclerosis in XCA images automatically. It employs a Quantum Convolutional Layer (QCL) that resembles a typical Convolutional Layer. However, unlike the convolution operation where a classical convolutional filter is applied, a QCL transforms the input using a quantum circuit. This QCL generates a multichannel image from a single channel XCA that feeds a traditional Convolutional Neural Network (CNN). The CNN was trained and optimized using two different architectures previously employed for atherosclerosis detection, viz. a DenseNet-based and a VGG-based architectures. Additionally, a comparison of Stochastic Gradient Descent (SGD) and SGD with Momentum (SGDM) were conducted. These architectures were analyzed and compared using a real dataset composed of 250 real XCA images (split into 125 images for testing and 125 for training) regarding their training without the QCL preprocessing and SGDM. The results showed that the CNNs trained with the outcome of the QCL surpasses the baseline CNNs performance. Herein, the best atherosclerosis detection reached an accuracy of 83.36%, while in other metrics such as precision, sensitivity, specificity, and F1-score, achieved an 81.52%, 86.67%, 83.99%, and 80.00%, respectively.
Background: Coronary angiography (CAG) is the cornerstone imaging modality for evaluating coronary artery stenosis and guiding interventional decision-making. However, interpretation based on single-frame angiographic images remains highly operator-dependent, and conventional deep learning models still face challenges in modeling complex vascular morphology and fine-grained texture patterns.Methods: We propose a Lightweight Quantum-Enhanced ResNet (LQER) for binary classification of coronary angiography images. A pretrained ResNet18 is employed as a classical feature extractor, while a parameterized quantum circuit (PQC) is introduced at the high-level semantic feature space for quantum feature enhancement. The quantum module utilizes data re-uploading and entanglement structures, followed by residual fusion with classical features, enabling end-to-end hybrid optimization with a strictly controlled number of qubits.Results: On an independent test set, the proposed LQER outperformed the classical ResNet18 baseline in accuracy, AUC, and F1-score, achieving a test accuracy exceeding 90%. The results demonstrate that lightweight quantum feature enhancement improves discrimination of positive lesions, particularly under class-imbalanced conditions.Conclusion: This study validates a practical hybrid quantum--classical learning paradigm for coronary angiography analysis, providing a feasible pathway for deploying quantum machine learning in medical imaging applications.
The prevalence of heart diseases is rising quickly throughout the world, which has an impact on both the world economy and public health. According to the recent statistical survey reports, the increasing mortality rate is due to high blood pressure, high cholesterol, the use of tobacco, obesity, and an inconsistent pulse rate. It is difficult and time-consuming to investigate the various variations of these factors and their impact on Coronary Artery Disease (CAD). Therefore, it is necessary to use modern approaches to diagnose the disease early and minimize the mortality rate. The fields of machine learning and data mining have a wide research dimension and various novel techniques that could help in the prediction of CAD in its early stages and identify their patterns and behaviors in a huge amount of data. The results of such predictions will aid the clinical staff in decision making and early diagnosis. In such a scenario, we proposed a quantum version of the Fully Convolutional Neural Network (FCQ-CNN) for Ischemic Heart Disease (IHD) classification. The proposed model evaluates the quantum circuit-based technique that was inspired by convolutional neural networks, a very successful machine learning model. This method providesO(log(n))O(log (n))O(log(n))depth fornnnqubits, reducing the number of parameters and allowing for effective training and testing of real quantum devices. The model has been evaluated by considering the IHD dataset after the data has been cleaned and filtered through the Maximally Relevant Minimally Redundancy (MRMR) filter. For dimension reduction, a Support Vector Machine along with Recursive Feature Elimination (SVM-RFE) has been considered. Initially, the model is tested with 20% of the whole dataset and gets the promising results of a testing accuracy of 84.6% with a testing loss of 0.28. By taking into account the same optimal parameters, the proposed model outcomes are compared to those of the classical Optimized Convolution Neural Network (Optimized-CNN) and Fully Connected Neural Network (FCNN) models. Comparing the model’s competency to that of earlier published quantum models yields improvements in accuracy of 8.6%, 12.6%, 3.5%, and 1.8% respectively.