It is often difficult to diagnose several lung illnesses, such as atelectasis and
cardiomegaly, as well as Pneumonia, in hospitals due to a scarcity of radiologists who
are educated in diagnostic imaging. If pneumonia is diagnosed early enough, the
survival rate of pulmonary patients suffering from the disease can be improved. Most
of the time, chest X-ray (CXR) pictures are used to detect and diagnose pneumonia.
When it comes to detecting pneumonia on CXR images, even an experienced
radiologist may have difficulty. It is vital to have an automated diagnostic system to
improve the accuracy of diagnostic results. It is estimated that automated pneumonia
detection in energy-efficient medical systems has a substantial impact on the quality
and cost of healthcare, as well as on response time. To detect pneumonia, we employed
deep transfer learning techniques such as ResNet-18 and VGG-16. Each of the model's
four standard metrics, namely accuracy, precision, recall, and f1-score, are used to
evaluate. The best model is established by the use of metrics. To make pneumonia
detection simple, the website is designed by employing the best model.
Keywords: Deep Learning, Performance Metrics, Pneumonia, ResNet-18, Streptococcus Pneumoniae.