Amalgamation of wavelet transform and neural network for COVID-19 detection Online publication date: Thu, 29-Feb-2024
by Madhu Jain; Renu Sharma
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 44, No. 2, 2024
Abstract: A zoonotic natured virus affecting almost every part of the globe is COVID-19. Early detection of such disease may lead to curable affairs. Since then, many research institutes have been trying to find effective methods for detecting and curing COVID-19. Real-time polymerase chain reaction test is also a method used for detection of the COVID-19. But, due to its accuracy rate and availability of kit, it is not relied on. Here, a combination of machine learning and wavelet transform based algorithm for chest X-ray classification is proposed. Image pre-processing is done using wavelet transform and further the classification is done using convolution neural network. It is a multi-class classifier, which will classify whether input image is COVID-19 affected, pneumonia or not affected. The dataset collected for this study from an open-source repository. It comprises 2,550 images of each class. For quantitative analysis of the proposed architecture, parameters such as accuracy, precision, F1 score, recall and sensitivity are measured.
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