Lung nodule CT medical image analysis based on deep learning

Abstract

In recent years, lung cancer has become one of the most lethal factors to human beings. Clinical data show that the probability of lung nodules developed into lung cancer is about 30%. Due to the lack of obvious symptoms, around 70% of lung cancer patients in China are in advanced stage of lung cancer when firstly diagnosed. Therefore, early identification of lung nodules is of great significance for early diagnosis and therapy. Currently, artificial intelligence has been widely used to generate predictive model of lung nodules by learning algorithms adapted to image characteristics, leading to improved accuracy and higher sensitivity of diagnosis of early lung cancer. In this work, Luna16 (lung nodule analysis 2016, containing a total of 888 low-dose chest Computed Tomography (CT) thin-slice plain scan lesions) were selected as the data set, providing a total of 1018 CT slices with the most representative shape of lung nodules in this analysis. Next, this project was performed on Baidu AI Studio platform, applying both U-Net and PSP Net to train a model of rapid detection of lung nodules. The training process generated a model providing a rapid and accurate identification of lung nodules larger than 3 mm in diameter. Results showed that the accuracy of U-Net was higher than that of PSP Net, indicating a high potential in further clinical diagnosis in lung cancer.