Comparative Analysis of Pneumonia Detection from Chest X-ray Using Deep Learning

In health sector, various automated system has been proposed for detection of pneumonia. This study represents survey on pneumonia detection from chest X-ray. In this study usability, seamless, goodness factor, computational complexities of the algorithms, various methodologies, and their limitation is to be analyzed. This study provide help to select the most effective and efficient methods from a real perspective. The purpose of this study is analysis and comparison of various methodologies related to the detection of pneumonia. This study also discusses the quality, usability and size off available X-ray datasets. After comparisons of studies, mostly available datasets are highly imbalanced and limited. Advanced pre-processing techniques are required; quality plays a major role to increase the performance, so that significant features are to be extracted in deep learning. Some parameters which are more clinically applicable like Positive likelihood ratio (PLR), Negative likelihood ratio (NLR), Positive predictive value (PPV) and Negative predictive value (NPV) are considered to reflect clinical efficiency.