Classifying the severity of medical images using the quantum-classical model with respect to the desperately required COVID-19 virus screening area. It is a pandemic concern that is having an impact on millions of people’s lives. The most frequent result that involves expert evaluation in COVID-19 Computed Tomography(CT) images of the lungs is ground glass turbidity. Quantum Machine Learning (QML), is one of the fields that quantum computing has the ability to explore. These days, with so much data available, machine learning might gain from increased speed, computational complexity in which it needs a lot of weights for better performance. In this paper, exploring a machine learning issue using a quantum technique and quantum-classical hybrid model use for severity classification that perform on different input images 28*28, 32*32 and 64*64 that’s run on simulator. The research laid out in this paper shows that the hybrid model succeeds over CNN in terms of low resolution image size and fewer trainable parameters. Quantum-Classical model categorizes the patient’s Computed Tomography images into three severity classes: moderate, severe and critical. The best hybrid model in this study uses a 32*32 image size with accuracy of 96.82% and 3075 trainable parameters , whereas the VGG-19 model uses a 64*64 image size with 262659 trainable parameters provide accuracy of 85.97%.
Classifying the severity of medical images using the quantum-classical model with respect to the desperately required COVID-19 virus screening area. It is a pandemic concern that is having an impact on millions of people’s lives. The most frequent result that involves expert evaluation in COVID-19 Computed Tomography(CT) images of the lungs is ground glass turbidity. Quantum Machine Learning (QML), is one of the fields that quantum computing has the ability to explore. These days, with so much data available, machine learning might gain from increased speed, computational complexity in which it needs a lot of weights for better performance. In this paper, exploring a machine learning issue using a quantum technique and quantum-classical hybrid model use for severity classification that perform on different input images 2828, 3232 and 6464 that’s run on simulator. The research laid out in this paper shows that the hybrid model succeeds over CNN in terms of low resolution image size and fewer trainable parameters. Quantum-Classical model categorizes the patient’s Computed Tomography images into three severity classes: moderate, severe and critical. The best hybrid model in this study uses a 3232 image size with accuracy of 96.82% and 3075 trainable parameters , whereas the VGG-19 model uses a 64*64 image size with 262659 trainable parameters provide accuracy of 85.97%.
Practical quantum computing (QC) is still in its in-fancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it is a major challenge and a cause for harsh restrictions in contemporary QC. In this paper, we apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. That is, we apply hybrid quantum transfer learning to an image processing task in the field of medical image processing. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.
BACKGROUND: In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. METHODS: This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. RESULTS: The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. CONCLUSION: The results suggest that quantum neural networks outperform in COVID-19 traits' classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.
This research presents a hybrid quantum-classical machine learning framework for medical image classification under noisy conditions using chest X-ray images. A classical Convolutional Neural Network (CNN) model and a hybrid quantum-classical model integrating variational quantum circuits were implemented using PyTorch and PennyLane. Gaussian noise was introduced during validation to evaluate model robustness under image perturbations. Experimental results demonstrated comparable performance between the classical CNN and the hybrid quantum-classical model, highlighting the potential of Quantum Machine Learning (QML) for future healthcare AI applications. GitHub Repository:https://github.com/saichandukundarapu/quantum-covid-detection
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.
Artificial intelligence (AI) is assisting in several aspects of the COVID-19 pandemic, including medical diagnosis and therapy, drug development, molecular research and epidemiology. The involvement of AI in healthcare can help doctors to detect symptoms more quickly. In such era, we use the Quantum Machine Learning (QML) approaches to address clinical applications of machine learning (ML) technology, such as electronic healthcare data and clinical features. This paper presents the two QML algorithms, i.e, Enhanced Quantum Support Vector Machine (E-QSVM) and Quantum Random Forest (QRF) applied to COVID-19 and influenza datasets, which were collected from different private hospitals. The experimental results show that the proposed models outperform in terms of accuracy by achieving the highest accuracy of 78% for the E-QSVM model and 75% for the QRF model respectively. The competency of the models is obtained by comparing them with classical models and recently published quantum models.
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.