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Journal of Theoretical and Applied Information Technology

A HYBRID QUANTUM-CLASSICAL FRAMEWORK FOR PNEUMONIA DETECTION FROM CHEST X-RAY IMAGES USING EFFICIENTNET-B4 AND DATA RE-UPLOADING VARIATIONAL QUANTUM CIRCUITS

V. R. NIVEDITHA, SASHI KANTH BETHA, DHRUVA R. RINKU, VASU GERA, RISHTI, TANISHA GOYAL, RENY JOSE, D.VETRITHANGAM

Published Sep 15, 2026• 0 citations
Publisher

Abstract

This paper presents a hybrid quantum-classical framework for binary pneumonia classification from chest X-ray images. A fine-tuned EfficientNet-B4 convolutional backbone serves as a fixed feature extractor, producing 1,792-dimensional representations that are compressed through a classical bridge network to eight scalar values. These values are subsequently processed by an 8-qubit Variational Quantum Circuit (VQC) that employs the data re-uploading strategy, encoding input features before each of three variational layers, for a total of 48 learnable quantum parameters. All quantum operations are performed using the PennyLane Lightning Qubit simulator; no physical quantum hardware was used, and the results therefore do not constitute a demonstration of quantum advantage over classical methods. The model is evaluated on the Kaggle Chest X-Ray (Pneumonia) benchmark (5,856 images) using a single train/validation/test split (5,016/200/624 images, with the 200-image validation set sampled from the training data); no cross-validation, repeated training runs, or statistical significance testing were performed, and the reported figures should accordingly be read as a single-run result rather than as estimates with associated confidence intervals. At a classification threshold of t = 0.59 selected on the test set itself, and therefore subject to optimistic bias, the model achieves 93.75% accuracy, 93.98% precision, 96.15% recall, 95.06% F1-score, and 0.9729 ROC-AUC. A two-phase training procedure a frozen-backbone stage followed by selective layer unfreezing, together with a feature pre-extraction cache- was used to make simulation-based quantum training practical on a single consumer GPU; both measures are reported for reproducibility rather than as research contributions in themselves. EigenCAM visualizations are provided as a qualitative illustration of model attention and suggest, without independent radiologist verification, that the model tends to attend to lung parenchyma regions broadly consistent with radiological pneumonia indicators; these interpretability observations should be treated as preliminary.

DOI
10.5281/zenodo.23292018

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This paper presents a hybrid quantum-classical framework for binary pneumonia classification from chest X-ray images. A fine-tuned EfficientNet-B4 convolutional backbone serves as a fixed feature extractor, producing 1,792-dimensional representations that are compressed through a classical bridge network to eight scalar values. These values are subsequently processed by an 8-qubit Variational Quantum Circuit (VQC) that employs the data re-uploading strategy, encoding input features before each of three variational layers, for a total of 48 learnable quantum parameters. All quantum operations are performed using the PennyLane Lightning Qubit simulator; no physical quantum hardware was used, and the results therefore do not constitute a demonstration of quantum advantage over classical methods. The model is evaluated on the Kaggle Chest X-Ray (Pneumonia) benchmark (5,856 images) using a single train/validation/test split (5,016/200/624 images, with the 200-image validation set sampled from the training data); no cross-validation, repeated training runs, or statistical significance testing were performed, and the reported figures should accordingly be read as a single-run result rather than as estimates with associated confidence intervals. At a classification threshold of t = 0.59 selected on the test set itself, and therefore subject to optimistic bias, the model achieves 93.75% accuracy, 93.98% precision, 96.15% recall, 95.06% F1-score, and 0.9729 ROC-AUC. A two-phase training procedure a frozen-backbone stage followed by selective layer unfreezing, together with a feature pre-extraction cache- was used to make simulation-based quantum training practical on a single consumer GPU; both measures are reported for reproducibility rather than as research contributions in themselves. EigenCAM visualizations are provided as a qualitative illustration of model attention and suggest, without independent radiologist verification, that the model tends to attend to lung parenchyma regions broadly consistent with radiological pneumonia indicators; these interpretability observations should be treated as preliminary.
Abstract Pneumonia remains a leading cause of mortality globally, necessitating rapid and accurate diagnostic tools to assist radiologists. While classical Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), have demonstrated high efficacy in medical image analysis, they are often characterized by high computational complexity and massive parameter requirements. This research proposes a novel Hybrid Quantum-Classical Neural Network (HQCNN) to detect Pneumonia from chest X-ray images with enhanced computational efficiency. By integrating a classical ResNet-18 architecture for feature extraction with a Variational Quantum Circuit (VQC) based on PennyLane, we leverage the quantum mechanical principles of superposition and entanglement for pattern recognition. The proposed model was trained and validated on a standard chest X-ray dataset and benchmarked against a classical transfer learning equivalent. Experimental results indicate that the hybrid quantum model achieved a validation accuracy of 78% with significantly fewer trainable parameters (~ 24) compared to the classical baseline (62% accuracy, ~ 1026 parameters). Furthermore, the quantum model demonstrated superior sensitivity (100% Recall), successfully identifying all positive pneumonia cases in the validation batch. This study highlights the potential of Quantum Machine Learning (QML) as a resource-efficient, high-sensitivity alternative for future medical diagnostic systems.
INTRODUCTION: Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a quantum deep neural network (QDNN). Therefore, we introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting pneumonia using 5863 X-ray scans with binary cases. METHODS: The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10-6 and six layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs. RESULTS: The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy gained by the convolutional neural network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method. CONCLUSION: This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the speed and accuracy of the network in the clinical and healthcare sectors.
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.
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.
The problem of classifying brain tumours using magnetic resonance imaging (MRI) is a fundamental one in the field of computeraided diagnosis, since although many commonly employed convolutional neural network (CNN) architectures provide high accuracy they contain a large number of parameters, which has led to a search for more compact network designs. In this paper a hybrid quantum–classical approach is proposed that combines a lightweight CNN feature extractor with a trainable variational quantum circuit (VQC) in order to carry out four-class brain tumour classification. The MRI images are resized to 128 by 128 pixels and then processed by a small three-block CNN that reduces each image to an 8-dimensional feature vector. This vector is put into an 8-qubit quantum register by means of angle embedding and then acted on by a variational circuit composed of strongly entangling layers, by measuring each qubit in the Pauli-Z basis. The quantum features thus obtained are then fed into classical dense layers that categorise the images as glioma, meningioma, pituitary tumour or no tumour. The framework was tested on the Brain Tumor MRI dataset (7,200 images) and compared with a classical CNN baseline that was trained under the same conditions. The hybrid model reached a test accuracy of 76.88%, as compared to 72.31% for the classical baseline an improvement of 4.57 percentage points together with corresponding increases in macro-averaged precision, recall and F1-score. Notably, the hybrid model needed only 72 additional trainable parameters when compared to the classical baseline. The fact that the quantum layer was assessed using a noiseless simulator rather than on actual quantum hardware means that these results should be seen as indicating the feasibility of using compact quantum feature transformations for medical image classification rather than as showing a quantum advantage.
Quantum machine learning has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data domains such as medical imaging. In this work, we present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset, a standardized benchmark for distinguishing between benign and malignant breast tumors. Our architecture integrates classical convolutional feature extraction with two distinct quantum circuits: an amplitude-encoding variational quantum circuit (VQC) and an angle-encoding VQC circuit with circular entanglement, both implemented on four qubits. These circuits generate quantum feature embeddings that are fused with classical features to form a joint feature space, which is subsequently processed by a fully connected classifier. To ensure fairness, the hybrid QCNN is parameter-matched against a baseline classical CNN, allowing us to isolate the contribution of quantum layers. Both models are trained under identical conditions using the Adam optimizer and binary cross-entropy loss. Experimental evaluation in five independent runs demonstrates that the hybrid QCNN achieves statistically significant improvements in classification accuracy compared to the classical CNN, as validated by a one-sided Wilcoxon signed rank test (p = 0.03125) and supported by large effect size of Cohen's d = 2.14. Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks. This work establishes a statistical validation framework for assessing hybrid quantum models in biomedical applications and highlights pathways for scaling to larger datasets and deployment on near-term quantum hardware.