Enhancing Pneumonia Diagnosis: An Ensemble of Deep CNN Architectures for Accurate Chest X-Ray Image Analysis

Various organisms, such as bacterial and viral infections, can cause a lung infection known as pneumonia. It is a significant health concern, particularly in developing and underdeveloped countries with high pollution rates, overcrowding, and limited healthcare infrastructure. In order to effectively treat pneumonia and improve survival rates, early detection is essential. The simplest technique for identifying pneumonia is a chest X-ray (CXR) study, but CXR analysis can be subjective and challenging. In this paper, we have developed a method for automatically detecting pneumonia from CXR images by combining transfer learning and an ensemble of three CNN network architectures (InceptionV3, MobileNetV2, and Xception) with the weighted average ensemble method. We evaluated our approach on chest X-ray datasets, achieving a maximum accuracy of 92% and F1-scores of 87% and 92% for normal and pneumonia, respectively. Our proposed method outperforms existing ensemble techniques and other cutting-edge approaches, demonstrating the potential for improving pneumonia diagnosis through deep learning-based approaches.