A Systematic Review: Classification of Lung Diseases from Chest X-Ray Images Using Deep Learning Algorithms

• Published in: SN computer science Vol. 5; no. 4; p. 405
• Main Authors: Hage Chehade, Aya, Abdallah, Nassib, Marion, Jean-Marie, Hatt, Mathieu, Oueidat, Mohamad, Chauvet, Pierre
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 06.04.2024; Springer Nature B.V; Springer
• Subjects: Algorithms; Artificial intelligence; Chest; Classification; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; COVID-19; Data Structures and Information Theory; Datasets; Deep learning; Engineering Sciences; Image classification; Information Systems and Communication Service; Lung cancer; Lung diseases; Machine learning; Medical diagnosis; Medical imaging; Pattern Recognition and Graphics; Performance evaluation; Pneumonia; Research methodology; Review Article; Software Engineering/Programming and Operating Systems; Systematic review; Tuberculosis; Vision; X-rays; United States > US; COVID-19; Deep learning; Pneumonia; Tuberculosis; Preprocessing; Multi-label classification; Chest X-ray images; Lung diseases; Multi-class classification; Systematic review; Pulmonary nodule
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-02751-2

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.