Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray

Abstract

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.

19 pages, 5 figures, 3 tables

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis