Computer-aided detection in chest radiography based on artificial intelligence: a survey

By Chunli Qin, Demin Yao, Yonghong Shi, Zhijian Song. 2018

327 citations recorded by the source corpus.

As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.

Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

1. Keewon Shin
2. Namkug Kim
3. Ryoungwoo Jang
4. Hyun-Jin Bae
5. Jihye Yun
6. Enzo Ferrante
7. Yongwon Cho

Citations in the corpus

• Deep Learning in Medical Imaging (2019, methodology): “4,5 This kind of task has been executed based on the various ML algorithms with proper optimization, theoretical or empirical approaches.”
• PadChest: A large chest x-ray image dataset with multi-label annotated reports (2019, background): “4 Number of hidden LSTM units 64, 128, 256, 512 128 Number of hidden LSTM layers [1, 3] 2”
• Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis (2020, methodology): “A special type of deep neural network, known as CNNs, was used to implement the CAD system (19, 20).”