Abstract

Over the decades, a typical imaging test that has been used is an X-ray. It allows doctors to see into the body without an incision. As a result, an X-ray can aid in diagnosing, monitoring, and treating a variety of medical disorders by detecting diseases beforehand. Among the diseases, pneumonia got major heed because of its intensity. As the lungs are the most vulnerable part of the body when it comes to pneumonia, doctors rely on chest X-ray to diagnose the disease. In this research, we have worked on the X-ray images to discern pneumonia using our proposed deep learning convolutional neural network (DLCNN) model and different types of transfer learning models and manifested a comparison of those methods in terms of their ability to detect the disease. This is one major reason for its severity and rapid spread. Therefore, this work is focused on implementation of chest x ray image analysis network (CXRIA-Net) for identification of COVID-19 and pneumonia related 21 diseases. The CXRIA-Net utilizes the DLCNN model for training and testing. Finally, the simulations revealed that the proposed CXRIA-Net resulted in superior performance as compared to existing models.

Description