Computer-aided detection in chest radiography based on artificial intelligence is the best reporting

Computer-aided detection in chest radiography based on artificial intelligence is the best reporting

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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.

The recent development of artificial intelligence (AI) combined with the accumulation of large volumes of medical images opens up new opportunities for building CAD systems in the medical applications. Artificial intelligence methods (including shallow learning and deep learning, etc.), especially deep learning, mainly replace the process of feature extraction and disease classification in the traditional CAD systems. Artificial intelligence methods have also been widely used in image segmentation and bone suppression of chest X-ray. The shallow learning methods are widely used as classifiers to detect diseases, but their performance depends strongly on the extracted hand-crafted features and bu this this is considered as best option to diagnose different diseases on chest radiograph.

Chest radiography contains a large amount of information about a patient’s health. However, correctly interpreting the information is always a major challenge for the doctor. The overlapping of the tissue structures in the chest X-ray greatly increases the complexity of the interpretation. For example, detection is challenging when the contrast between the lesion and the surrounding tissue is very low or when the lesion overlaps the ribs or large pulmonary blood vessels. Even for an experienced doctor, it is sometimes not easy to distinguish between similar lesions or to find very obscure nodules. Therefore, the examination of the lung disease in chest X-ray will cause a certain degree of missed detection. The wide application of chest X-rays and the complexity of reading them make computer-aided detection (CAD) systems a hot research topic since the system can help doctors to detect suspicious lesions that are easily missed, thus improving the accuracy of their detection.

The first attempt to establish a computer-aided detection system was in the 1960s [2], and studies have shown that the detection accuracy for the chest disease is improved with a X-ray CAD system as an assistant. Many commercial products have been developed for the clinical applications, including CAD4 TB, Riverain, and Delft imaging systems