Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification o
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ORIGINAL PAPER
Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules Jumin Zhao 1,2 & Chen Zhang 1 & Dengao Li 2,3 & Jing Niu 1
# Society for Imaging Informatics in Medicine 2020
Abstract Lung cancer has the highest mortality rate of all cancers, and early detection can improve survival rates. In the recent years, lowdose CT has been widely used to detect lung cancer. However, the diagnosis is limited by the subjective experience of doctors. Therefore, the main purpose of this study is to use convolutional neural network to realize the benign and malignant classification of pulmonary nodules in CT images. We collected 1004 cases of pulmonary nodules from LIDC-IDRI dataset, among which 554 cases were benign and 450 cases were malignant. According to the doctors’ annotates on the center coordinates of the nodules, two 3D CT image patches of pulmonary nodules with different scales were extracted. In this study, our work focuses on two aspects. Firstly, we constructed a multi-stream multi-task network (MSMT), which combined multi-scale feature with multiattribute classification for the first time, and applied it to the classification of benign and malignant pulmonary nodules. Secondly, we proposed a new loss function to balance the relationship between different attributes. The final experimental results showed that our model was effective compared with the same type of study. The area under ROC curve, accuracy, sensitivity, and specificity were 0.979, 93.92%, 92.60%, and 96.25%, respectively. Keywords Convolutional neural network . Pulmonary nodule classification . Multi-scale feature fusion . Multi-task learning
Introduction Lung cancer has the highest mortality rate among all cancer diseases in the world and poses a great threat to human health. Statistics showed that the number of new cases and deaths of lung cancer in 2018 ranked first, accounting for 11.8% of total new cases of cancer and 18.4% of total deaths of cancer, respectively [1]. In most cases, it is difficult to detect lung cancer at an early stage, and it is too late to treat patients once their initial symptoms begin. However, according to the American Cancer Society, if lung cancer is detected early,
* Dengao Li [email protected] 1
College of Information and Computer, Taiyuan University of Technology, Jinzhong, China
2
Technology Research Center of Spatial Information Network Engineering of Shanxi, Jinzhong, China
3
College of Data Science, Taiyuan University of Technology, Jinzhong, China
the survival rate can reach 47% [2]. Therefore, early and accurate interpretation of nodules is of great significance for the prevention and treatment of lung cancer. In recent years, low-dose CT imaging technology has become increasingly mature, has been widely used in clinical examinations, and also has great advantages in the screening of pulmonary nodules [3]. Pulmonary nodules are round, opaque local parenchymal lesions with a diameter less than 3–4 cm [4]. In c
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