Abstract
A viral outbreak with a lower respiratory tract febrile illness causes pulmonary syndrome named COVID-19. Pulmonary consolidations developed in the lungs of the patients are imperative factors during prognosis and diagnosis. Existing Deep Learning techniques demonstrate promising results in analyzing X-ray images when employed with Transfer Learning. However, Transfer Learning has its inherent limitations, which can be prevaricated by employing the Progressive Resizing technique. The Progressive Resizing technique reuses old computations while learning new ones in Convolution Neural Networks (CNN), enabling it to incorporate prior knowledge of the feature hierarchy. The proposed classification model can classify pulmonary consolidation into normal, pneumonia, and SARS-CoV-2 classes by analyzing X-rays images. The method exhibits substantial enhancement in classification results when the Transfer Learning technique is applied in consultation with the Progressive Resizing technique on EfficientNet CNN. The customized VGG-19 model attained benchmark scores in all evaluation criteria over the baseline VGG-19 model. GradCam based feature interpretation, coupled with X-ray visual analysis, facilitates improved assimilation of the scores. The model highlights its strength to assist medical experts in the COVID-19 identification during the prognosis and subsequently for diagnosis. Clinical implications exist in peripheral and remotely located health centers with the paucity of trained human resources to interpret radiological investigations' findings.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.