Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss
Joint Authors
Tran, Giang Son
Nghiem, Thi Phuong
Nguyen, Van Thi
Luong, Chi Mai
Burie, Jean-Christophe
Source
Journal of Healthcare Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-04
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer.
In this paper, we propose a new deep learning method to improve classification accuracy of pulmonary nodules in computed tomography (CT) scans.
Our method uses a novel 15-layer 2D deep convolutional neural network architecture for automatic feature extraction and classification of pulmonary candidates as nodule or nonnodule.
Focal loss function is then applied to the training process to boost classification accuracy of the model.
We evaluated our method on the LIDC/IDRI dataset extracted by the LUNA16 challenge.
The experiments showed that our deep learning method with focal loss is a high-quality classifier with an accuracy of 97.2%, sensitivity of 96.0%, and specificity of 97.3%.
American Psychological Association (APA)
Tran, Giang Son& Nghiem, Thi Phuong& Nguyen, Van Thi& Luong, Chi Mai& Burie, Jean-Christophe. 2019. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1175242
Modern Language Association (MLA)
Tran, Giang Son…[et al.]. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss. Journal of Healthcare Engineering No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1175242
American Medical Association (AMA)
Tran, Giang Son& Nghiem, Thi Phuong& Nguyen, Van Thi& Luong, Chi Mai& Burie, Jean-Christophe. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1175242
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175242