Detection and Classification of Pneumonia Using DenseNet169

A considerable number of individuals struggle with pneumonia, a condition caused by viruses which is most prevalent in emerging and impoverished nations where there is an inadequate number of medical services, contaminated and jam-packed environments, and other issues. Pericardial effusion, a disorder where fluid fills the lungs and makes breathing difficult, is brought on by pneumonia. Early diagnosing pneumonia is an important action that must be taken in order to enhance the chance of survival and develop services for therapy. The efficient development of predictive algorithms can be made easier by the artificial intelligence discipline of deep learning. There are several methods for identifying pneumonia, including pulse oximetry, CT scanning, and many more, but X-ray tomography is the most often used method. In this study, pneumonia can be identified and categorized via a deep learning (DL) system which employs DenseNet169. Neural Networks (NN) on the DenseNet169 yields accuracy values of 86%, 84.11% and 89% for SVM, Naive Bayes and RF respectively.