ABSTRACT
A chest X-ray is a quick and efficient way to test a patient's severity level, which helps doctors to view vital organs. With the help of a chest X-ray, 14 different diseases can be detected and diagnosed by the doctor. The lack of publicly available datasets makes it difficult to provide more computer-aided detection in real-world medical science with chest X-rays. The ensemble method uses one more model with classification. It mostly reduces the training time and overfitting as well as improves the classification. In classification, the convolutional neural network (CNN) model predicts the image class, such as COVID-19, tuberculosis, or pneumonia. The CNN model differentiates healthy and infected lung X-ray images in this case. In this chapter, we try integrating the automated detection system with a standard hospital management system. The proposed system model worked on three image datasets on the CNN model. This chapter concludes with the calculation of various performance metrics.
