A Systematic Review: Classification of Lung Diseases from Chest X-Ray Images Using Deep Learning Algorithms - Hage Chehade, Aya; Abdallah, Nassib; Marion, Jean-Marie; Hatt, Mathieu; Oueidat, Mohamad; Chauvet, Pierre (SN computer science, 2024)
The purpose of this survey is to provide a comprehensive review of the most recent publications on lung disease classification from chest X-ray images using deep learning algorithms. Methods: This research aims to present several common chest radiography datasets and to introduce briefly the general image preprocessing procedures that are applied to chest X-ray images. Then, the classification of specific and multiple lung diseases is described, focusing on the method and dataset used in the selected studies, the evaluation measures and the results. In addition, the problems and future direction of lung diseases classification are discussed to provide an important research base for researchers in the future. As the most common examination tool, Chest X-ray (CXR) is crucial in the medical field for disease diagnosis. Thus, the classification of chest diseases based on chest X-ray has gained significant attention from researchers. In recent years, deep learning methods have been used and have emerged as powerful techniques in medical imaging fields. One hundred ten articles published from 2016 to 2023 were reviewed and summarized, confirming that this particular research area is very important and has great potential for future research.