Classification Of Chest X-ray Images Of Covid-19 By Deep Learning Based CNN Model and Attention Mechanism

Covid-19 is a highly infectious viral disease that has been found in a broad range of animal species, including humans. This fatal virus threatens not just people’s lives, but also their health and the country’s economy. Although Covid-19 is a serious and widespread disease, there is presently no vaccine available to protect against it. Clinical research conducted on people who contracted COVID-19 found that the respiratory system was the most common location of infection after exposure to the virus. When it comes to the diagnosis of lung-related illnesses, imaging modalities such as chest CT and chest x-ray (also known as radiography) are superior. The cost of a chest CT scan is more than that of a thorough chest x-ray, but the latter is much cheaper. When it comes to machine learning, deep learning provides the most impressive results. It provides valuable insight that may be used to the investigation of a large number of chest x-ray images, which may have a substantial impact on the Covid19 screening procedure. Specifically, this research will apply the attention method on the resnet50 features. Six thousand four hundred thirty-two chest x-ray scan samples were generated once the feature learning process was finished using the Xgboost method for validation in the Kaggle repository. These were split between 965 validation examples and 5467 training examples. The proposed model (resnet-attention-xgboost) obtained 98.34 percent, while the supplemented dataset reached 99 percent, when it came to identifying chest X-ray pictures. This is in comparison to earlier models. This study is purely concerned with prospective categorization methodologies for patients infected with covid-19.