(untitled) (Journal of Pharmaceutical Negative Results, 2023)
Nowadays, to diagnose or detect any diseases in humans, we must have a good diagnosis to predict the disease that is present in the human body. In general, we aim to employ X-Ray, CT, or MRI scan techniques for lung disease prediction. Artificial intelligence and deep learning methods advances have aided in the diagnosis and classification of lung diseases in medical imaging. As a result, there has been a lot of research on utilizing artificial intelligence to identify lung diseases. This article presents deep learning approaches that may be used to identify lung diseases based on medical scans. This study’s main objective is to explore pulmonary infection in early stage by making use of a variety of medical images to recognize, diagnose, and classify different types of lung infections via the use of deep learning methods for early detection of lung disease. A detailed description of how these procedures have been used to treat lung nodules, TB, pneumonia, lung cancer, COVID-19, and ILD is also presented. Finally, the application of deep learning algorithms, machine learning algorithms and transfer learning to medical images is reviewed, as well as an appraisal of future difficulties and prospective possibilities.