3D multi‐resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules. Issue 1 (6th August 2021)

• Record Type: Journal Article
• Authors: Fu, Yu; Xue, Peng; Zhao, Peng; Li, Ning; Xu, Zhuodong; Ji, Huizhong; Zhang, Zhili; Cui, Wentao; Dong, Enqing
• Is Part Of: International journal of imaging systems and technology. Volume 32:Issue 1(2022)
• Page Start: 74
• Page End: 87
• Publication Date: 2021-08-06
• Subjects: deep learning -- multiple pathological types -- multi‐resolution method -- pulmonary nodules; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22642
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: To accurately diagnose multiple pathological types of pulmonary nodules based on lung computed tomography (CT) images, a multi‐resolution three‐dimensional (3D) multi‐classification deep learning model (Mr‐Mc) was proposed. The Mr‐Mc model was constructed by using our own constructed lung CT image dataset of pulmonary nodules with clinical pathological information (LCID‐CPI), which can accurately diagnose inflammation, squamous cell carcinoma, adenocarcinoma, and other benign diseases. In order to process nodules with different sizes, a multi‐resolution extraction method was proposed to extract 3D volume data with different resolutions from lung CT images. The Mr‐Mc was composed of three different resolution networks, each of which has input volume data of a specific resolution. Experiments showed that the constructed Mr‐Mc model can achieve an average accuracy of 0.81 on LCID‐CPI. Besides, the Mr‐Mc model can also achieve a high accuracy of 0.87 on the Lung Image Database Consortium and Image Database Resource Initiative dataset.