Recent advancement of deep learning techniques for pneumonia prediction from chest X-ray image
Md. Rabiul Hasan, Shah Muhammad Azmat Ullah, Sheikh Md. Rabiul Islam
Abstract
Pneumonia is a life-threatening, acute lung infection found all over the world that mostly affects the lungs. Computer vision-related automatic detection algorithms are currently highly used in research areas like medical imaging. Deep learning algorithms have enabled some impressive improvements in medical diagnosis in recent years. This study provides a summary of a recently developed DL-based pneumonia diagnosis system as well as important details about the data sets used for the training and testing of those networks. Additionally, it emphasizes the ensemble learning and deep transfer learning methodologies as well as the many performance measurements created by researchers in this field. The most recent research publications are reviewed here and collected from different sources like Scopus, Google Scholar, PubMed, ResearchGate, and IEEE Xplore databases using the terms “Pneumonia”, “Deep-Learning”, “X-Ray” and “CNN”. The most current works are organized according to a taxonomy for easier understanding. Lastly, we addressed the limitations in deploying deep learning methods to the detection of pneumonia and potential future developments in this field of study. This study aims to assist experts in select the most suitable and effective methods for pneumonia detection.