Implementation of a quantum machine learning model for the categorization and analysis of COVID-19 cases

Abstract

Coronavirus is a new pathogen that causes both the upper and lower respiratory systems. The global COVID-19 pandemic’s size, rate of transmission, and the number of deaths is all steadily rising. COVID-19 instances could be detected and analyzed using Computed Tomography scanning. For the identification of lung infection, chest CT imaging has the advantages of speedy detection, relatively inexpensive, and high sensitivity. Due to the obvious minimal information available and the complicated image features, COVID-19 identification is a difficult process. To address this problem, modified-Deformed Entropy (QDE) algorithm for CT image scanning is suggested. To enhance the number of training samples for effective testing and training, the suggested method utilizes QDE to generate CT images. The retrieved features are used to classify the results. Rapid innovations in quantum mechanics had prompted researchers to use Quantum Machine Learning (QML) to test strategies for improvement. Furthermore, the categorization of corona diagnosed, and non-diagnosed pictures is accomplished through Quanvolutional Neural Network (QNN). To determine the suggested techniques, the results are related with other methods. For processing the COVID-19 imagery, the study relates QNN with other existing methods. On comparing with other models, the suggested technique produced improved outcomes. Also, with created COVID-19 CT images, the suggested technique outperforms previous state-of-the-art image synthesis techniques, indicating possibilities for different machine learning techniques such as cognitive segmentation and classification. As a result of the improved model training/testing, the image classification results are more accurate.