With millions of medical data being produced daily, it is important to leverage current technology to improve the healthcare system. As traditional deep learning models are unable to effectively handle high-dimensional data, a hybrid classical-quantum approach is used to enhance traditional deep learning algorithms. The hybrid classical-quantum models are built upon the classical deep learning framework with quantum techniques. The classical layers are utilized for feature extraction while the quantum layer is used for computational tasks. This addresses computational limitations and data imbalance challenges in medical image analysis. Transfer learning models, such as ResNet18 and InceptionV3, are used in conjunction with a quantum variational circuit to perform the task. The model is trained on a chest X-ray dataset, which consists of two sets of data: binary and multiclass. The binary dataset consists of Normal and COVID-19 images, while the multiclass dataset has COVID-19, Normal, and Tuberculosis images for classification. PennyLane, as a simulator, has been used to perform quantum computing. The dataset has also been used to train the traditional transfer learning models, pre-trained ResNet18, and their results have been compared. Out of all the models tested, the hybrid classical-quantum model, namely Hybrid ResNet18, performed the best. It achieved an accuracy of 93.22% for the binary classification task and 93.91% for the multiclass classification. The results show that there is a statistically significant improvement in classification accuracy and F1-score of the hybrid approach over the classical transfer learning baselines under a simulation-based environment. This sets the stage for further research on real-world physical quantum hardware. Class imbalance and overlapping predictions have been addressed using methods such as data augmentation and confusion matrix analysis. Thus, quantum machine learning could be used as a vital tool to analyze medical data for better diagnostic results.
With millions of medical data being produced daily, it is important to leverage current technology to improve the healthcare system. As traditional deep learning models are unable to effectively handle high-dimensional data, a hybrid classical-quantum approach is used to enhance traditional deep learning algorithms. The hybrid classical-quantum models are built upon the classical deep learning framework with quantum techniques. The classical layers are utilized for feature extraction while the quantum layer is used for computational tasks. This addresses computational limitations and data imbalance challenges in medical image analysis. Transfer learning models, such as ResNet18 and InceptionV3, are used in conjunction with a quantum variational circuit to perform the task. The model is trained on a chest X-ray dataset, which consists of two sets of data: binary and multiclass. The binary dataset consists of Normal and COVID-19 images, while the multiclass dataset has COVID-19, Normal, and Tuberculosis images for classification. PennyLane, as a simulator, has been used to perform quantum computing. The dataset has also been used to train the traditional transfer learning models, pre-trained ResNet18, and their results have been compared. Out of all the models tested, the hybrid classical-quantum model, namely Hybrid ResNet18, performed the best. It achieved an accuracy of 93.22% for the binary classification task and 93.91% for the multiclass classification. The results show that there is a statistically significant improvement in classification accuracy and F1-score of the hybrid approach over the classical transfer learning baselines under a simulation-based environment. This sets the stage for further research on real-world physical quantum hardware. Class imbalance and overlapping predictions have been addressed using methods such as data augmentation and confusion matrix analysis. Thus, quantum machine learning could be used as a vital tool to analyze medical data for better diagnostic results.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.
Abstract Classical deep learning models often struggle to fully capture complex visual patterns and to generalize effectively when trained on limited or imbalanced medical imaging data. To address this limitation, this paper presents a novel Hybrid Quantum Deep Learning (Hybrid QDL) framework that leverages the strengths of both deep neural networks and quantum circuits for advanced medical image interpretation. The proposed model integrates a pre-trained EfficientNet-B4 as the feature extractor and a variational quantum circuit as the classification head, enabling joint learning of hierarchical and quantum-enhanced representations. The Hybrid QDL model is evaluated on three clinically relevant datasets: COVIDx (for 3-class COVID-19 diagnosis using chest X-rays), PneumoniaMNIST (for pneumonia detection), and OrganAMNIST (for multi-organ abdominal classification). The model achieves state-of-the-art results, with 98.48% accuracy and 98.33% F1-score on COVIDx, outperforming both classical and prior quantum baselines. On PneumoniaMNIST and OrganAMNIST, it reaches 98.28% and 95.27% accuracy, respectively, along with high macro F1-scores. Ablation studies confirm that increasing quantum circuit depth and entanglement enhances discriminative power, particularly in complex multi-class scenarios. These results demonstrate that the Hybrid QDL framework is a robust, scalable solution for automated medical diagnosis and holds significant promise for real-world clinical decision support and COVID-19 detection.
Abstract Data scarcity and the computationally expensive nature of deep learning hinder its applications for medical image classification. This paper explored hybrid quantum-classical models for classifying chest X-rays in data-restricted environments. ResNet18, MobileNetV2, and its hybrid quantum variants were trained at 100%, 50%, 20% and 5% training sets respectively. The model was assessed by accuracy, F1-score, and the area under the curve (AUC). It was found that classical models had better accuracy compared to hybrid models, whereas hybrid models improved the AUC and hence class separability. In a low-data regime, MobileNet performed better due to its computationally lightweight nature, and the best performing Hybrid model combining efficiency with robustness is the MobileNet + Quantum model, revealing promising medical applications for the models where data is scarce. Keywords: Quantum Machine Learning, Deep Learning, MobileNet, ResNet, Chest X-ray, Medical Imaging
Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Quantum–Classical Convolutional Neural Networks (HQCNNs) have been developed, using quantum properties like superposition and entanglement to improve feature extraction and classification of medical images. This review offers a thorough analysis of 50 peer-reviewed studies published from 2021 to 2026 and introduces a taxonomy of HQCNN architectures. It classifies existing approaches into three categories: Quantum-to-Classical, Classical-to-Quantum, and Hybrid Quantum Transfer Learning. The studies reviewed encompass a diverse range of medical imaging applications, including binary and multi-class disease classification using X-ray, CT, MRI, ultrasound, dermoscopic, retinal, and mammographic images. Additionally, the review compares HQCNNs with their classical CNN counterparts, exploring how factors like quantum circuit design, dataset size, class imbalance, task complexity, evaluation protocols, and hardware limitations influence classification performance. It concludes with an analysis of the key limitations of current HQCNN models, discusses challenges in deploying NISQ devices practically, and proposes future research directions to bridge the gap between experimental HQCNN frameworks and reliable clinical diagnostic systems.
Abstract The coronavirus has been affecting people's everyday life since 2019. To stop the further spread of the virus, early detection of carriers of the virus and isolation are needed. Due to the limitations of nuclear accounting amplification assay (NAAT), CT imaging, which has the high sensitivity to the new corona virus, is used as a diagnostic method. In this study, a classical-quantum hybrid model using a variational quantum circuit as a classifier was designed. A pre-trained VGG16 was used for feature extraction, and the extracted features were passed to a variational quantum classifier for classification. Experiments show that using variational quantum circuits to linearly vary the classical data can improve the accuracy of the model prediction. Hybrid Model achieves good results with values of 98.78%, 97.65%, 100.00%, and 98.81% for Accuracy, Precision, Recall, and F1-score metrics, respectively. The comparison between Hybrid Model and the classical model shows that the overall performance of Hybrid Model is better than that of the classical model. Hybrid Model provides a reliable, flexible and scalable classical-quantum deep learning approach for the prediction task of COVID-19.
This research presents a new hybrid model that combines the capabilities of a pre-trained ResNet-50 with a Quantum Neural Network (QNN) to enhance the classification of MRI brain images for tumor recognition. Combining conventional deep learning and quantum computing, this technique competes with traditional models regarding accuracy, speed, and computational effectiveness. The ResNet-50 architecture is the foundation for feature extraction, using its demonstrated ability to handle challenging MRI image data. ResNet-50's linear layer weights are converted into angular parameters to take advantage of the potential of quantum computing, allowing the quantum layer to function within a six-angle depth framework. This unique integration allows the model to efficiently execute binary classification and multi-class tumor detection. The hybrid model outperforms conventional models, providing quicker processing speeds and higher classification accuracy, especially when dealing with high-dimensional and noisy data. The model optimizes 512-dimensional data using four qubits, allowing for improved feature representation and better insights into complicated medical imaging patterns. The quantum layer's resistance to noise enhances the model's accuracy, establishing a new standard for medical diagnostic tools. This novel integration of classical and quantum approaches offers a significant advance in MRI-based tumor identification, providing higher performance and opening the path for next-generation medical imaging systems.