دورية أكاديمية

Pulmonary Nodule Classification Based on Heterogeneous Features Learning

التفاصيل البيبلوغرافية
العنوان: Pulmonary Nodule Classification Based on Heterogeneous Features Learning
المؤلفون: Tong, C, Liang, B, Su, Q, Yu, M, Hu, J, Bashir, AK, Zheng, Z
بيانات النشر: IEEE
سنة النشر: 2021
المجموعة: eSpace - Manchester Metropolitan University's Research Repository
الوصف: IEEE Pulmonary cancer is one of the most dangerous cancers with a high incidence and mortality. An early accurate diagnosis and treatment of pulmonary cancer can observably increase the survival rates, where computer-aided diagnosis systems can largely improve the efficiency of radiologists. In this paper, we propose a deep automated lung nodule diagnosis system based on three-dimensional convolutional neural network (3D-CNN) and support vector machine (SVM) with multiple kernel learning (MKL) algorithms. The system not only explores the computed tomography (CT) scans, but also the clinical information of patients like age, smoking history and cancer history. To extract deeper image features, a 34-layers 3D Residual Network (3D-ResNet) is employed. Heterogeneous features including the extracted image features and the clinical data are learned with MKL. The experimental results prove the effectiveness of the proposed image feature extractor and the combination of heterogeneous features in the task of lung nodule diagnosis.
نوع الوثيقة: article in journal/newspaper
وصف الملف: text
اللغة: English
العلاقة: https://e-space.mmu.ac.uk/626674Test/; https://ieeexplore.ieee.org/document/9181623Test; https://e-space.mmu.ac.uk/626674/1/2020-%20IEEE%20JSAC-%20Pulmonary%20Nodule%20Classification%20Based%20on.pdfTest; Tong, C , Liang, B , Su, Q , Yu, M , Hu, J , Bashir, AK and Zheng, Z (2021) Pulmonary Nodule Classification Based on Heterogeneous Features Learning. IEEE Journal on Selected Areas in Communications, 39 (2). pp. 574-581. ISSN 0733-8716
الإتاحة: https://e-space.mmu.ac.uk/626674/1/2020-%20IEEE%20JSAC-%20Pulmonary%20Nodule%20Classification%20Based%20on.pdfTest
حقوق: info:eu-repo/semantics/openAccess
رقم الانضمام: edsbas.4B159A61
قاعدة البيانات: BASE