Publication

Abstract : This study investigates the attractive area of AI-powered pneumonia detection using chest X-rays. Recognizing the global significance of pneumonia, it explores various research methodologies to improve the accuracy and speed of diagnosis. The paper highlights the potential of AI in offering precise and timely diagnosis through an examination of ensemble models, deep learning architectures, problems with data quality, and hybrid AI frameworks. Still, there are problems such as the need for strong generalization, potential biases, and insufficient data. The study demonstrates how various strategies and hybrid models might cooperate to produce diagnostic tools that are more flexible and effective. This poll highlights the critical role that early detection plays in enhancing patient outcomes, which adds to the crucial conversation on AI’s role in healthcare. Additionally, it establishes the foundation for upcoming advancements in AI-powered pneumonia detection. © IFIP International Federation for Information Processing 2024.