Ensemble-Based Computer-Aided Pneumonia Identification on Chest X-Ray Images

A potentially severe lung disease caused by many viral infections is called pneumonia. Because of its similarities to other lung disorders, pneumonia can be challenging to diagnose and control on chest X-ray images. Therefore, the current methodologies cannot achieve significant rates of efficiency in pneumonia prediction. This research provides an Ensemblebased computer-aided pneumonia identification to streamline the evaluation technique for chest X-ray images. The approach relies on several pre-trained CNN deep learning models, which researchers have previously used to improve effectiveness in various medical applications. We suggest pre-trained deep learning models over the ImageNet dataset, such as CNN, DenseNet121, VGG16, ResNet, and InceptionNet. The chest Xray data set was utilized to train these models. At the end of the study, the features taken out of these five models were combined to provide the results. With an accuracy score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 8. 2 2 \%}$</tex>, the suggested ensemble technique performed better than other cutting-edge techniques.