Deep Convlutional models on medical data for detection of Covid Lung X-Rays through Transfer Learning

ABSTRACT

Lung cancer patients have a greater frequency of COVID-19 infection, pulmonary issues, and worse survival rates as compared to the general population. The world's major professional organisations released new recommendations for the diagnosis, treatment, and follow-up of lung cancer patients as a guide for prioritising cancer care issues during the epidemic. 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. In order to extract the features I needed for this study, I used feature extraction from transfer learning, which involves importing a pre-trained CNN model such as the Distributed Deep Convolutional Inception model, Distributed Deep Convolutional VGGNet model, or Distributed Deep Convolutional with ResNet Model and altering the final layer to suit my needs.The model can achieve an F1-score of 0.88 using Distributed Deep Convolutinal VGGNet, the highest of all pretrained models. Additionally, the COVID-19-related X-rays are broken down into three severity levels moderate, medium, and severe. The data are analysed using the F1-Score because precision and recall are both crucial elements in this investigation. 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. The proposed strategies have had a significant impact on the nation as a warning to society.