Abstract
BackgroundA deep learning computer artificial intelligence system is helpful for early identification of ground glass opacities (GGOs).MethodsImages from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were used in AlexNet and GoogLeNet to detect pulmonary nodules, and 221 GGO images provided by Xinhua Hospital were used in ResNet50 for detecting GGOs. We used computed tomography image radial reorganization to create the input image of the three-dimensional features, and used the extracted features for deep learning, network training, testing, and analysis.ResultsIn the final evaluation results, we found that the accuracy of identification of lung nodule could reach 88.0%, with an F-score of 0.891. In terms of performance and accuracy, our method was better than the existing solutions. The GGO nodule classification achieved the best F-score of 0.87805. We propose a preprocessing method of red, green, and blue (RGB) superposition in the region of interest to effectively increase the differentiation between nodules and normal tissues, and that is the innovation of our research.ConclusionsThe method of deep learning proposed in this study is more sensitive than other systems in recent years, and the average false positive is lower than that of others.
Highlights
A deep learning computer artificial intelligence system is helpful for early identification of ground glass opacities (GGOs)
Ye et al BioMed Eng OnLine (2019) 18:6 lung adenocarcinoma are usually diagnosed when they are in the advanced stages, and median survival time after diagnosis is usually less than 1 year [4]
The recognition of GGO is based on a subjective assessment of lung attenuation at computed tomography (CT), but observation of pulmonary nodules by doctors is labor-intensive and time-consuming, and because of personal differences, the results of examination may often be different
Summary
A deep learning computer artificial intelligence system is helpful for early identification of ground glass opacities (GGOs). Pulmonary ground-glass opacity (GGO) is defined as a hazy opacity that does not obscure the underlying bronchial structures or pulmonary vessels on high-resolution computed tomography [1]. Ye et al BioMed Eng OnLine (2019) 18:6 lung adenocarcinoma are usually diagnosed when they are in the advanced stages, and median survival time after diagnosis is usually less than 1 year [4]. The recognition of GGO is based on a subjective assessment of lung attenuation at CT, but observation of pulmonary nodules by doctors is labor-intensive and time-consuming, and because of personal differences, the results of examination may often be different
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