The chest X-ray findings can improve the diagnosis time-cycle with enhanced screening capability. Since the coronavirus consolidation is dissimilar to bacterial or viral pneumonia consolidation, the radiographs help identify the COVID-19 infection. As the infections in the lungs can be screened with radiographs, the radiographs are being used in the diagnostic workup, check disease progression, and follow-up of the pulmonary consolidations. However, the RTPCR test's reliability with higher turnaround time poses a challenge in diagnosis, especially in developing nations due to limited medical facilities. A nasopharyngeal Exudate swab sample is screened in the RT-PCR test. Įtiological tests, Reverse-Transcription Polymerase chain reaction test, Chest X-rays, and Chest Computed Tomography Scans (CT-Scans) are the tests/techniques which can identify the infection. The cumulative number of confirmed cases crossed 14,79,168 globally, with approximately 87,987 virus-related deaths as of April 09, 2020, with a significant spread worldwide. The World Health Organization named the pulmonary syndrome “COronaVIrus Disease 2019” (COVID-19) or severe acute respiratory syndrome coronavirus2 (SARS-CoV-2). Bronchoalveolar lavage (BAL) test analysis highlighted an unfamiliar coronavirus strain responsible for the outbreak. In November 2019, a similar viral outbreak with a lower respiratory tract febrile illness was reported in China. In the last two decades, viral epidemics like Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), H1N1 influenza, and the Middle East Respiratory Syndrome CoronaVirus (MERS-CoV) have drawn significant attention. World Health Organization (WHO) reported viral emergences over numerous occurrences, which epitomizes a severe concern for public health. Clinical implications exist in peripheral and remotely located health centers with the paucity of trained human resources to interpret radiological investigations' findings. The model highlights its strength to assist medical experts in the COVID-19 identification during the prognosis and subsequently for diagnosis. GradCam based feature interpretation, coupled with X-ray visual analysis, facilitates improved assimilation of the scores. The customized VGG-19 model attained benchmark scores in all evaluation criteria over the baseline VGG-19 model. ![]() 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 proposed classification model can classify pulmonary consolidation into normal, pneumonia, and SARS-CoV-2 classes by analyzing X-rays images. 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. ![]() However, Transfer Learning has its inherent limitations, which can be prevaricated by employing the Progressive Resizing technique. Existing Deep Learning techniques demonstrate promising results in analyzing X-ray images when employed with Transfer Learning. ![]() Pulmonary consolidations developed in the lungs of the patients are imperative factors during prognosis and diagnosis. A viral outbreak with a lower respiratory tract febrile illness causes pulmonary syndrome named COVID-19.
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