Volume 20 No 12 (2022)
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Detection of COVID-Lung Cancer through X-ray data through Distributed Deep Convolutional VGGNet and ResNet Models
Syamala Bathula , K Sreenivasa Rao
When compared to the general population, lung cancer patients have a higher incidence of COVID-19 infection, pulmonary problems, and poorer survival results. As a reference for prioritising cancer care issues during the epidemic, the world's main professional organisations issued new recommendations for the diagnosis, treatment, and follow-up of lung cancer patients. In the modern world, we are battling COVID-2019, a coronavirus-driven pandemic that is among the worst in human history. If the infection is discovered early, the patient can receive treatment right away (before it enters the lower respiratory tract). once the infection has reached the lungs, to look for ground-glass opacity on the chest X-ray caused by fibrosis. Based on the significant differences in the X-ray images of an infected and non-infected person, artificial intelligence systems can be used to determine the presence and severity of illness. For this study, I employed feature extraction from Transfer Learning, which entails importing a pre-trained CNN model, such as Distributed Deep Convolutional VGGNet or Distributed Deep Convolutional with ResNet Model, and changing the last layer to meet my needs.Using Distributed Deep ConvolutinalVGGNet, the model can get an F1-score of 0.88, which is the best among all pretrained models. Furthermore, the X-rays suggesting COVID-19 are divided into three categories based on severity: mild, medium, and severe. Because precision and recall are both essential factors in this study, the data are analysed using the F1-Score. The confusion matrix and the results for the F1-Score, Precision, Recall, and overall Accuracy are also supplied to give a full analysis of the Model performance.As a warning to society, the proposed approaches have had a considerable impact upon the country.
covid x rays, ,Resnet50,VGG16 Covid-19,x-rays,ML,DL,CT hadoop
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