Evaluating Deep Learning Models for Accurate Pneumonia Diagnosis from Chest X-Rays

Pneumonia remains one of the most widespread and deadly chest-related diseases, posing a major global health challenge. It significantly impacts health outcomes, especially within at-risk groups like young children and the elderly. The World Health Organization (WHO) reports that pneumonia has caused the deaths of over 808,000 children under the age of five in 2017, and older adults are also at elevated risk. Early and accurate diagnosis by radiologists is crucial to improving patient outcomes and saving lives. This study examines alternative deep-learning models to To offer support to radiologists in the early detection of pneumonia using pictures of chest X-ray from a Kaggle dataset. The models tested include convolutional neural networks (CNNs), Siamese networks, the Xception model, and RESNET50. The outcomes represent that the Xception model stands out with an astonishing accuracy of 98 percentage, making it the most powerful model for early pneumonia diagnosis among those tested. The outcomes or findings of this study establish a solid basis for future investigations focused on improving deep learning models for the early detection of pneumonia, ultimately contributing to better health outcomes and saving lives.