Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray
This survey provides a comprehensive overview of lung disease diagnosis via medical imaging for researchers and practitioners in medical image analysis, consolidating information on methods and datasets.
This paper surveys the detection and classification of pulmonary lesions using CT and X-ray imaging over the last decade. It reviews methods for lung nodules, pneumonia, and other common lesions, introduces 26 public medical image datasets, and discusses current challenges and future research directions.
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention. These diseases can be effectively diagnosed and treated with the help of lung imaging. With the development of deep learning technology and the emergence of many public medical image datasets, the diagnosis of lung diseases via medical imaging has been further improved. This article reviews pulmonary CT and X-ray image detection and classification in the last decade. It also provides an overview of the detection of lung nodules, pneumonia, and other common lung lesions based on the imaging characteristics of various lesions. Furthermore, this review introduces 26 commonly used public medical image datasets, summarizes the latest technology, and discusses current challenges and future research directions.