A novel lung nodule accurate detection of computerized tomography images based on convolutional neural network and probability graph model. (9th July 2022)

• Record Type: Journal Article
• Authors: Xia, Xunpeng; Zhang, Rongfu; Yao, Xufeng; Huang, Gang; Tang, Tiequn
• Is Part Of: Computational intelligence. Volume 38:Number 5(2022)
• Page Start: 1728
• Page End: 1747
• Publication Date: 2022-07-09
• Subjects: convolution neural network -- interslice changes -- lung nodule detection -- probability graph model; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12531
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Precisely detecting lung nodules from original computerized tomography (CT) images is a critical technology in the earlier screening of lung cancer. Therefore, the domain of accurate detection has gradually attracted the attention of researchers. However, due to the complex characteristics of pulmonary nodules and the limitations of CT imaging property, detecting nodules with high accuracy from lung CT images is a challenging task. This article proposes an effective and robust detection network to accurately detect lung nodules by innovatively implementing a probability graph model in the candidate detection and false‐positive reduction phase. Different from previous works which use complex 3‐dimensional image information to reduce false positives, we propose two effective probability graph mechanisms, which analyze multiscale information and continuous slices (interslice changes) motion information to improve performance. We evaluated our method on an open‐source LIDC‐IDRI dataset which contains a total of 243, 958 CT images and achieved high‐precision lung nodule detection results (sensitivity score of 0.945). Via introducing multiscale information and the dynamic information of the interslice, the task of lung nodule detection obtains higher precision detection results than other similar methods.