Review: Machine Learning based Lung Diseases Classification by Using Chest X-Ray Image

Lung disorders are quite harmful, especially in an emerging and low-income countries where the millions of people were exposed to the air pollution and live in poverty. According to World Health Organization (WHO), indoor air pollution-related diseases like pneumonia and asthma result in over four million avoidable deaths annually. With diseases including pneumonia, TB, lung-cancer, & COVID-19 contributing to high rates of morbidity and mortality, lung diseases continue to pose a serious threat to world health. Chest X-rays (CXRs) are among the most popular and economical imaging modalities, and traditional diagnostic approaches frequently depend on clinical evaluations and imaging procedures. New approaches to increasing the precision and effectiveness of lung disease classification using CXR image have been made possible by recent developments in machine learning (ML) and deep learning (DL). This paper examines current advancements for common lung disorders, the use of imaging modalities, and machine learning models. Additionally, the study investigates a number of publicly accessible datasets that are utilized for well-known lung conditions.