Implementation of the Deep Learning-based Website For Pneumonia Detection & Classification

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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.