Pulmonary nodule risk classification in adenocarcinoma from CT images using deep CNN with scale transfer module. Issue 8 (14th May 2020)

• Record Type: Journal Article
• Authors: Zheng, Jie; Yang, Dawei; Zhu, Yu; Gu, Wanghuan; Zheng, Bingbing; Bai, Chunxue; Zhao, Lin; Shi, Hongcheng; Hu, Jie; Lu, Shaohua; Shi, Weibin; Wang, Ningfang
• Is Part Of: IET image processing. Volume 14:Issue 8(2020)
• Page Start: 1481
• Page End: 1489
• Publication Date: 2020-05-14
• Subjects: lung -- learning (artificial intelligence) -- cancer -- biological organs -- medical image processing -- image classification -- patient diagnosis -- neural nets -- feature extraction -- computerised tomography; pulmonary nodule risk classification -- CT images -- deep CNN -- scale transfer module -- lung cancer -- clinical treatment decision -- early diagnosis -- lung adenocarcinoma -- imaging studies -- deep convolutional neural network -- named STM‐Net -- different resolution images -- computed tomography database -- Zhongshan Hospital Fudan University -- lung adenocarcinomas risk -- minimally invasive adenocarcinoma -- authors -- risk stage prediction -- classification accuracy -- pulmonary nodules classification -- physicians diagnosis pulmonary nodules risk classification -- early‐stage; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0248
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Pulmonary nodules risk classification in adenocarcinoma is essential for early detection of lung cancer and clinical treatment decision. Improving the level of early diagnosis and the identification of small lung adenocarcinoma has been always an important topic for imaging studies. In this study, the authors propose a deep convolutional neural network (CNN) with scale‐transfer module (STM) and incorporate multi‐feature fusion operation, named STM‐Net. This network can amplify small targets and adapt to different resolution images. The evaluation data were obtained from the computed tomography (CT) database provided by Zhongshan Hospital Fudan University (ZSDB). All data have a pathological label and their lung adenocarcinomas risk are classified into four categories: atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma. The authors' deep learning network STM‐Net was trained and tested for the risk stage prediction. The accuracy and the average area under the receiver operating characteristic curve achieved by their method are 95.455% and 0.987 for the ZSDB dataset. The experimental results show that STM‐Net largely boosts classification accuracy on the pulmonary nodules classification compared with state‐of‐the‐art approaches. The proposed method will be an effective auxiliary to help physicians diagnosis pulmonary nodules risk classification in adenocarcinoma in early‐stage.