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.
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.
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.
Pneumonia poses a significant global health challenge, demanding accurate and timely diagnosis. While deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in medical image analysis for pneumonia detection, CNNs often suffer from high computational costs, limitations in feature representation, and challenges in generalizing from smaller datasets. To address these limitations, we explore the application of Quanvolutional Neural Networks (QNNs), leveraging quantum computing for enhanced feature extraction. This paper introduces a novel hybrid quantum-classical model for pneumonia detection using the PneumoniaMNIST dataset. Our approach utilizes a quanvolutional layer with a parameterized quantum circuit (PQC) to process 2×2 image patches, employing rotational Y-gates for data encoding and entangling layers to generate non-classical feature representations. These quantum-extracted features are then fed into a classical neural network for classification. Experimental results demonstrate that the proposed QNN achieves a higher validation accuracy of 83.33% compared to a comparable classical CNN which achieves 73.33%. This enhanced convergence and sample efficiency highlight the potential of QNNs for medical image analysis, particularly in scenarios with limited labeled data. This research lays the foundation for integrating quantum computing into deep learning-driven medical diagnostic systems, offering a computationally efficient alternative to traditional approaches.
Deep Neural Networks have offered numerous innovative solutions to brain-related diseases including Alzheimer’s. However, there are still a few standpoints in terms of diagnosis and planning that can be transformed via quantum Machine Learning (QML). In this study, we present a hybrid classical–quantum machine learning model for the detection of Alzheimer’s using 6400 labeled MRI scans with two classes. Hybrid classical–quantum transfer learning is used, which makes it possible to optimally pre-process complex and high-dimensional data. Classical neural networks extract high-dimensional features and embed informative feature vectors into a quantum processor. We use resnet34 to extract features from the image and feed a 512-feature vector to our quantum variational circuit (QVC) to generate a four-feature vector for precise decision boundaries. Adam optimizer is used to exploit the adaptive learning rate corresponding to each parameter based on first- and second-order gradients. Furthermore, to validate the model, different quantum simulators (PennyLane, qiskit.aer and qiskit.basicaer) are used for the detection of the demented and non-demented images. The learning rate is set to 10−4 for and optimized quantum depth of six layers, resulting in a training accuracy of 99.1% and a classification accuracy of 97.2% for 20 epochs. The hybrid classical–quantum network significantly outperformed the classical network, as the classification accuracy achieved by the classical transfer learning model was 92%. Thus, a hybrid transfer-learning model is used for binary detection, in which a quantum circuit improves the performance of a pre-trained ResNet34 architecture. Therefore, this work offers a method for selecting an optimal approach for detecting Alzheimer’s disease. The proposed model not only allows for the automated detection of Alzheimer’s but would also speed up the process significantly in clinical settings.
Recently, Quantum Computing has started being widely used for classical problem solving. We can use Quantum mechanics principles for problem solving. CNN (Convolutional Neural Network) had been used in classical computing and problem solving for many years, the implementation of this neural network model in Quantum devices and circuits is known as QCNN (Quantum Convolutional Neural Network). QCNNs are built using quantum circuits. QCNNs can read complicated pictures more quickly, they are allegedly better at extracting features and identifying patterns, these features can make it extremely good at image classification. By analyzing multiple features at the same time, QCNNs process data faster than classical CNNs and reduce the reliance on big datasets, therefore it addresses difficulties related to limited data. We can also see how robust QCNN is to noise, maintaining good accuracy despite many changes in image quality. Good speed and accuracy are demonstrated in experimental results on training datasets, indicating that QCNNs have the potential to transform medical diagnoses and enhance patient outcomes. We have made use of the chest x-ray image dataset which have three labels, Covid, Pneumonia, and Normal.
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 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.