3D multi‐view squeeze‐and‐excitation convolutional neural network for lung nodule classification. Issue 3 (1st February 2023)

• Record Type: Journal Article
• Authors: Yang, Yang; Li, Xiaoqin; Fu, Jipeng; Han, Zhenbo; Gao, Bin
• Is Part Of: Medical physics. Volume 50:Issue 3(2023)
• Page Start: 1905
• Page End: 1916
• Publication Date: 2023-02-01
• Subjects: attention mechanism -- consistency analysis deep learning -- image processing -- lung nodule classification; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.16221
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Early screening is crucial to improve the survival rate and recovery rate of lung cancer patients. Computer‐aided diagnosis system (CAD) is a powerful tool to assist clinicians in early diagnosis. Lung nodules are characterized by spatial heterogeneity. However, many attempts use the two‐dimensional multi‐view (MV) framework to learn and simply integrate multiple view features. These methods suffer from the problems of not capturing the spatial characteristics effectively and ignoring the variability of multiple views. In this paper, we propose a three‐dimensional MV convolutional neural network (3D MVCNN) framework and embed the squeeze‐and‐excitation (SE) module in it to further address the variability of each view in the MV framework. Methods: First, the 3D multiple view samples of lung nodules are extracted by the spatial sampling method, and a 3D CNN is established to extract 3D abstract features. Second, build a 3D MVCNN framework according to the 3D multiple view samples and 3D CNN. This framework can learn more features of different views of lung nodules, taking into account the characteristics of spatial heterogeneity of lung nodules. Finally, to further address the variability of each view in the MV framework, a 3D MVSECNN model is constructed by introducing a SE module in the feature fusion stage. For training and testing purposes we used independent subsets of the public LIDC‐IDRI dataset. Results: For the LIDC‐IDRI dataset, this study achieved 96.04% accuracy and 98.59% sensitivity in the binary classification, and 87.76% accuracy in the ternary classification, which was higher than other state‐of‐the‐art studies. The consistency score of 0.948 between the model predictions and pathological diagnosis was significantly higher than that between the clinician's annotations and pathological diagnosis. Conclusions: The results show that our proposed method can effectively learn the spatial heterogeneity of nodules and solve the problem of multiple view variability. Moreover, the consistency analysis indicates that our method can provide clinicians with more accurate results of benign‐malignant lung nodule classification for auxiliary diagnosis, which is important for assisting clinicians in clinical diagnosis.