Deep Learning Techniques for Detecting Lung Diseases

Deep learning has emerged as a powerful tool in the medical imaging field that aims to improve accuracy and speed of diagnosis. It can automatically learn feature representations from images which are then used to classify them as normal or abnormal. Several studies have shown the potential of deep learning in detecting lung diseases with high accuracy, even outperforming human experts in some cases. Deep learning models can also be used to predict disease progression and treatment outcomes, and to guide personalized treatment plans. This paper presents a study of the major deep learning techniques applied to medical imaging, focusing on pulmonary medical images, datasets, and benchmarks. The techniques include classification, detection, and segmentation tasks for various lung diseases, such as tuberculosis, lung cancer, pulmonary nodule diseases, pneumonia, asthma, COVID-19 and interstitial lung disease. The paper also discusses the challenges and potential directions for the future application of deep learning techniques in detecting lung disease. Deep learning has been successful in several domains, including acoustics, images, and natural language processing, and has the potential to significantly improve disease screening and diagnosis in the medical field