Abstract
COVID-19 is caused by the SARS coronavirus 2 family (SARS-CoV-2). A quick antibody or antigen test can detect the presence of COVID-19, but further testing is needed to confirm a positive result. Radiologists use chest X-rays to diagnose chest diseases early. The proposed system integrates discrete wavelet transformation and deep learning to help radiologists categorise conditions. Wavelets break down images into multiple spatial resolutions depending on a high pass and low pass frequency components and efficiently extract characteristics from lung X-rays. Here, we use a hybrid wavelet-CNN model to diagnose lung X-rays. The proposed CNN model is trained and verified on different source COVID 19 chest X-ray images for binary and three classes. The proposed studies suggest significant improvement in outcomes, with the best parameters achieving 99.42% accuracy and 96.43% accuracy for binary and three classes. The depiction of feature maps shows that our suggested network collected features from the corona virus-affected lung properly. Results suggest that the proposed model is successful enough for COVID 19 diagnosis.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
