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

TL;DR

This survey reviews recent advances in detecting and classifying pulmonary lesions using CT and X-ray imaging, highlighting datasets, techniques, challenges, and future directions in lung disease diagnosis.

Contribution

It provides a comprehensive overview of recent deep learning methods, datasets, and challenges in pulmonary lesion detection and classification over the past decade.

Findings

Summarizes 26 public medical image datasets.

Highlights recent deep learning techniques for lung lesion detection.

Discusses current challenges and future research directions.

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.