A Review on Deep Learning Based Automated Pneumonia Detection using X-ray images

Abstract

This paper provides a comprehensive literature survey on the application of various deep learning models for pneumonia detection using chest X-ray images. It reviews numerous studies that propose different architectures and techniques aimed at enhancing detection accuracy and efficiency. The survey covers the use of methods such as transfer learning and ensemble techniques to mitigate data scarcity and augment model performance. It also discusses the development of advanced algorithms for the precise identification and localization of pneumonia indications. Despite promising results, the survey acknowledges persisting challenges, including the need for high accuracy in medical testing and the limited availability of annotated medical images. This review underscores the transformative potential of deep learning in pneumonia detection and identifies areas for future research.