ABSTRACT
Respiratory diseases are among the most severe diseases in the world. With the outbreak in 2020, the mortality rate among people affected by respiratory diseases has gone up by a significant amount. Early detection of these diseases could help the patients to be able to receive treatment and cure the disease at an earlier stage. Existing manual diagnoses methods take up a significant amount of time for the results to be obtained. The use of Machine Learning techniques in the medical profession has been made possible by recent developments in the disciplines of Image classification and Deep learning, as well as the availability of numerous open-source datasets. Machine learning models used in diagnoses of lung diseases have decreased the time needed for detection and reduced the amount of manual work required. This research examines how various machine learning algorithms can be used to diagnose various lung conditions. The key goal of this paper is to visualize the various trends in lung disease diagnoses using machine learning and recognize the existing issues and the in this domain's possible future. The potential future in this domain can be explored by increasing the accuracy of existing systems and increasing the number of lung diseases detection applications that are aided by machine learning. © 2022 IEEE.