A systematic review: Deep learning-based methods for pneumonia region detection

TL;DR

This systematic review analyzes recent deep learning methods for pneumonia detection, highlighting their datasets, techniques, challenges, and future directions to improve diagnosis accuracy and efficiency.

Contribution

It provides a comprehensive summary of existing deep learning approaches for pneumonia detection, emphasizing their advantages, limitations, and potential research pathways.

Findings

Deep learning methods outperform traditional machine learning in pneumonia detection.

Current challenges include dataset limitations and model generalization issues.

Future work should focus on enhancing model robustness and dataset diversity.

Abstract

Pneumonia disease is one of the leading causes of death among children and adults worldwide. In the last ten years, computer-aided pneumonia detection methods have been developed to improve the efficiency and accuracy of the diagnosis process. Among those methods, the effects of deep learning approaches surpassed that of other traditional machine learning methods. This review paper searched and examined existing mainstream deep-learning approaches in the detection of pneumonia regions. This paper focuses on key aspects of the collected research, including their datasets, data processing techniques, general workflow, outcomes, advantages, and limitations. This paper also discusses current challenges in the field and proposes future work that can be done to enhance research procedures and the overall performance of deep learning models in detecting, classifying, and localizing infected regions. This review aims to offer an insightful summary and analysis of current research, facilitating the development of deep learning approaches in addressing treatable diseases.